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2010


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Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

Gomez Rodriguez, M., Grosse-Wentrup, M., Peters, J., Naros, G., Hill, J., Schölkopf, B., Gharabaghi, A.

In Proceedings of the 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (ICPR WBD 2010), pages: 36-39, (Editors: J. Richiardi and D Van De Ville and C Davatzikos and J Mourao-Miranda), IEEE, Piscataway, NJ, USA, 1st Workshop on Brain Decoding (WBD), August 2010 (inproceedings)

Abstract
Brain-Computer Interfaces (BCI) that rely upon epidural electrocorticographic signals may become a promising tool for neurorehabilitation of patients with severe hemiparatic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. Here, we show in a patient-based feasibility study that online classification of arm movement intention is possible. The intention to move or to rest can be identified with high accuracy (~90 %), which is sufficient for BCI-guided neurorehabilitation. The observed spatial distribution of relevant features on the motor cortex indicates that cortical reorganization has been induced by the brain lesion. Low- and high-frequency components of the electrocorticographic power spectrum provide complementary information towards classification of arm movement intention.

ei

PDF Web DOI [BibTex]

2010


PDF Web DOI [BibTex]


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libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

Mooij, JM.

Journal of Machine Learning Research, 11, pages: 2169-2173, August 2010 (article)

Abstract
This paper describes the software package libDAI, a free & open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discrete-valued variables. libDAI supports directed graphical models (Bayesian networks) as well as undirected ones (Markov random fields and factor graphs). It offers various approximations of the partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with other open source software packages for approximate inference is given. libDAI is licensed under the GPL v2+ license and is available at http://www.libdai.org.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion

Zhang, K., Chan, L.

Neurocomputing, 73(13-15):2580-2588, August 2010 (article)

Abstract
Convolutive blind source separation (BSS) usually encounters two difficulties—the filter indeterminacy in the recovered sources and the relatively high computational load. In this paper we propose an efficient method to convolutive BSS, by dealing with these two issues. It consists of two stages, namely, multichannel blind deconvolution (MBD) and learning the post-filters with the minimum filter distortion (MFD) principle. We present a computationally efficient approach to MBD in the first stage: a vector autoregression (VAR) model is first fitted to the data, admitting a closed-form solution and giving temporally independent errors; traditional independent component analysis (ICA) is then applied to these errors to produce the MBD results. In the second stage, the least linear reconstruction error (LLRE) constraint of the separation system, which was previously used to regularize the solutions to nonlinear ICA, enforces a MFD principle of the estimated mixing system for convolutive BSS. One can then easily learn the post-filters to preserve the temporal structure of the sources. We show that with this principle, each recovered source is approximately the principal component of the contributions of this source to all observations. Experimental results on both synthetic data and real room recordings show the good performance of this method.

ei

PDF PDF DOI [BibTex]


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Simulating Human Table Tennis with a Biomimetic Robot Setup

Mülling, K., Kober, J., Peters, J.

In From Animals to Animats 11, pages: 273-282, (Editors: Doncieux, S. , B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret), Springer, Berlin, Germany, 11th International Conference on Simulation of Adaptive Behavior (SAB), August 2010 (inproceedings)

Abstract
Playing table tennis is a difficult motor task which requires fast movements, accurate control and adaptation to task parameters. Although human beings see and move slower than most robot systems they outperform all table tennis robots significantly. In this paper we study human table tennis and present a robot system that mimics human striking behavior. Therefore we model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis. The resulting model is implemented on an anthropomorphic robot arm with 7 degrees of freedom using robotics methods. We verify the functionality of the model both in a physical realistic simulation of an anthropomorphic robot arm and on a real Barrett WAM.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Cooperative Cuts for Image Segmentation

Jegelka, S., Bilmes, J.

(UWEETR-1020-0003), University of Washington, Washington DC, USA, August 2010 (techreport)

Abstract
We propose a novel framework for graph-based cooperative regularization that uses submodular costs on graph edges. We introduce an efficient iterative algorithm to solve the resulting hard discrete optimization problem, and show that it has a guaranteed approximation factor. The edge-submodular formulation is amenable to the same extensions as standard graph cut approaches, and applicable to a range of problems. We apply this method to the image segmentation problem. Specifically, Here, we apply it to introduce a discount for homogeneous boundaries in binary image segmentation on very difficult images, precisely, long thin objects and color and grayscale images with a shading gradient. The experiments show that significant portions of previously truncated objects are now preserved.

ei

Web [BibTex]

Web [BibTex]


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Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Statistical image analysis and percolation theory

Langovoy, M., Wittich, O.

28th European Meeting of Statisticians (EMS), August 2010 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Adapting Preshaped Grasping Movements Using Vision Descriptors

Kroemer, O., Detry, R., Piater, J., Peters, J.

In From Animals to Animats 11, pages: 156-166, (Editors: Doncieux, S. , B. Girard, A. Guillot, J. Hallam, J.-A. Meyer, J.-B. Mouret), Springer, Berlin, Germany, 11th International Conference on Simulation of Adaptive Behavior (SAB), August 2010 (inproceedings)

Abstract
Grasping is one of the most important abilities needed for future service robots. In the task of picking up an object from between clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which are often not available. Therefore, methods are needed that execute grasps robustly even with imprecise information gathered only from standard stereo vision. We propose techniques that reactively modify the robot‘s learned motor primitives based on non-parametric potential fields centered on the Early Cognitive Vision descriptors. These allow both obstacle avoidance, and the adapting of finger motions to the object‘s local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Fast algorithms for total-variationbased optimization

Barbero, A., Sra, S.

(194), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2010 (techreport)

Abstract
We derive a number of methods to solve efficiently simple optimization problems subject to a totalvariation (TV) regularization, under different norms of the TV operator and both for the case of 1-dimensional and 2-dimensional data. In spite of the non-smooth, non-separable nature of the TV terms considered, we show that a dual formulation with strong structure can be derived. Taking advantage of this structure we develop adaptions of existing algorithms from the optimization literature, resulting in efficient methods for the problem at hand. Experimental results show that for 1-dimensional data the proposed methods achieve convergence within good accuracy levels in practically linear time, both for L1 and L2 norms. For the more challenging 2-dimensional case a performance of order O(N2 log2 N) for N x N inputs is achieved when using the L2 norm. A final section suggests possible extensions and lines of further work.

ei

PDF [BibTex]

PDF [BibTex]


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Magnetic Nanostructured Propellers

Fischer, P., Ghosh, A.

July 2010 (patent)

pf

[BibTex]

[BibTex]


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Biased Feedback in Brain-Computer Interfaces

Barbero, A., Grosse-Wentrup, M.

Journal of NeuroEngineering and Rehabilitation, 7(34):1-4, July 2010 (article)

Abstract
Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject‘s current skill level.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., Krause, A.

In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), pages: 1019-1028, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

Abstract
Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Relative Entropy Policy Search

Peters, J., Mülling, K., Altun, Y.

In Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence, pages: 1607-1612, (Editors: Fox, M. , D. Poole), AAAI Press, Menlo Park, CA, USA, Twenty-Fourth National Conference on Artificial Intelligence (AAAI-10), July 2010 (inproceedings)

Abstract
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients (Bagnell and Schneider 2003), many of these problems may be addressed by constraining the information loss. In this paper, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems.

am ei

PDF Web [BibTex]

PDF Web [BibTex]


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Varieties of Justification in Machine Learning

Corfield, D.

Minds and Machines, 20(2):291-301, July 2010 (article)

Abstract
Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution

Görür, D., Rasmussen, C.

Journal of Computer Science and Technology, 25(4):653-664, July 2010 (article)

Abstract
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the number of components. Nonparametric mixture models sidestep the problem of finding the “correct” number of mixture components by assuming infinitely many components. In this paper Dirichlet process mixture (DPM) models are cast as infinite mixture models and inference using Markov chain Monte Carlo is described. The specification of the priors on the model parameters is often guided by mathematical and practical convenience. The primary goal of this paper is to compare the choice of conjugate and non-conjugate base distributions on a particular class of DPM models which is widely used in applications, the Dirichlet process Gaussian mixture model (DPGMM). We compare computational efficiency and modeling performance of DPGMM defined using a conjugate and a conditionally conjugate base distribution. We show that better density models can result from using a wider class of priors with no or only a modest increase in computational effort.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Robust probabilistic superposition and comparison of protein structures

Mechelke, M., Habeck, M.

BMC Bioinformatics, 11(363):1-13, July 2010 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Inferring deterministic causal relations

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., Schölkopf, B.

In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages: 143-150, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

Abstract
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Reinforcement Learning by Relative Entropy Policy Search

Peters, J., Mülling, K., Altun, Y.

30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), 30, pages: 69, July 2010 (poster)

Abstract
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients, many of these problems may be addressed by constraining the information loss. In this book chapter, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems. We will also present a real-world applications where a robot employs REPS to learn how to return balls in a game of table tennis.

ei

PDF [BibTex]

PDF [BibTex]


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Cooperative Cuts: Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

24th European Conference on Operational Research (EURO XXIV), July 2010 (talk)

Abstract
We introduce cooperative cut, a minimum cut problem whose cost is a submodular function on sets of edges: the cost of an edge that is added to a cut set depends on the edges in the set. Applications are e.g. in probabilistic graphical models and image processing. We prove NP hardness and a polynomial lower bound on the approximation factor, and upper bounds via four approximation algorithms based on different techniques. Our additional heuristics have attractive practical properties, e.g., to rely only on standard min-cut. Both our algorithms and heuristics appear to do well in practice.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms

Bruzzone, L., Persello, C.

In pages: 3720-3723 , IEEE, Piscataway, NJ, USA, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2010 (inproceedings)

Abstract
This paper addresses the recent trends in machine learning methods for the automatic classification of remote sensing (RS) images. In particular, we focus on two new paradigms: semisupervised and active learning. These two paradigms allow one to address classification problems in the critical conditions where the available labeled training samples are limited. These operational conditions are very usual in RS problems, due to the high cost and time associated with the collection of labeled samples. Semisupervised and active learning techniques allow one to enrich the initial training set information and to improve classification accuracy by exploiting unlabeled samples or requiring additional labeling phases from the user, respectively. The two aforementioned strategies are theoretically and experimentally analyzed considering SVM-based techniques in order to highlight advantages and disadvantages of both strategies.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing

Bridle, S., Balan, S., Bethge, M., Gentile, M., Harmeling, S., Heymans, C., Hirsch, M., Hosseini, R., Jarvis, M., Kirk, D., Kitching, T., Kuijken, K., Lewis, A., Paulin-Henriksson, S., Schölkopf, B., Velander, M., Voigt, L., Witherick, D., Amara, A., Bernstein, G., Courbin, F., Gill, M., Heavens, A., Mandelbaum, R., Massey, R., Moghaddam, B., Rassat, A., Refregier, A., Rhodes, J., Schrabback, T., Shawe-Taylor, J., Shmakova, M., van Waerbeke, L., Wittman, D.

Monthly Notices of the Royal Astronomical Society, 405(3):2044-2061, July 2010 (article)

Abstract
We present the results of the GREAT08 Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to stimulate new ideas by presenting the problem to researchers outside the shear measurement community. Six GREAT08 Team methods were presented at the launch of the Challenge and five additional groups submitted results during the 6 month competition. Participants analyzed 30 million simulated galaxies with a range in signal to noise ratio, point-spread function ellipticity, galaxy size, and galaxy type. The large quantity of simulations allowed shear measurement methods to be assessed at a level of accuracy suitable for currently planned future cosmic shear observations for the first time. Different methods perform well in different parts of simulation parameter space and come close to the target level of accuracy in several of these. A number of fresh ideas have emerged as a result of the Challenge including a re-examination of the process of combining information from different galaxies, which reduces the dependence on realistic galaxy modelling. The image simulations will become increasingly sophis- ticated in future GREAT challenges, meanwhile the GREAT08 simulations remain as a benchmark for additional developments in shear measurement algorithms.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Source Separation and Higher-Order Causal Analysis of MEG and EEG

Zhang, K., Hyvärinen, A.

In Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 2010), pages: 709-716, (Editors: Grünwald, P. , P. Spirtes), AUAI Press, Corvallis, OR, USA, 26th Conference on Uncertainty in Artificial Intelligence (UAI), July 2010 (inproceedings)

Abstract
Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a twolayer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality in their time-varying variances (envelopes). The model is identified in two steps. We first propose a new source separation technique which takes into account the autocorrelations (which may be time-varying) and time-varying variances of the sources. The causality in the envelopes is then discovered by exploiting a special kind of multivariate GARCH (generalized autoregressive conditional heteroscedasticity) model. The resulting causal diagram gives the effective connectivity between the separated sources; in our experimental results on MEG data, sources with similar functions are grouped together, with negative influences between groups, and the groups are connected via some interesting sources.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery

Zhang, K., Schölkopf, B., Janzing, D.

In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages: 717-724, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

Abstract
In nonlinear latent variable models or dynamic models, if we consider the latent variables as confounders (common causes), the noise dependencies imply further relations between the observed variables. Such models are then closely related to causal discovery in the presence of nonlinear confounders, which is a challenging problem. However, generally in such models the observation noise is assumed to be independent across data dimensions, and consequently the noise dependencies are ignored. In this paper we focus on the Gaussian process latent variable model (GPLVM), from which we develop an extended model called invariant GPLVM (IGPLVM), which can adapt to arbitrary noise covariances. With the Gaussian process prior put on a particular transformation of the latent nonlinear functions, instead of the original ones, the algorithm for IGPLVM involves almost the same computational loads as that for the original GPLVM. Besides its potential application in causal discovery, IGPLVM has the advantage that its estimated latent nonlinear manifold is invariant to any nonsingular linear transformation of the data. Experimental results on both synthetic and realworld data show its encouraging performance in nonlinear manifold learning and causal discovery.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Remote Sensing Feature Selection by Kernel Dependence Estimation

Camps-Valls, G., Mooij, J., Schölkopf, B.

IEEE Geoscience and Remote Sensing Letters, 7(3):587-591, July 2010 (article)

Abstract
This letter introduces a nonlinear measure of independence between random variables for remote sensing supervised feature selection. The so-called Hilbert–Schmidt independence criterion (HSIC) is a kernel method for evaluating statistical dependence and it is based on computing the Hilbert–Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is easy to compute and has good theoretical and practical properties. Rather than using this estimate for maximizing the dependence between the selected features and the class labels, we propose the more sensitive criterion of minimizing the associated HSIC p-value. Results in multispectral, hyperspectral, and SAR data feature selection for classification show the good performance of the proposed approach.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Clustering stability: an overview

von Luxburg, U.

Foundations and Trends in Machine Learning, 2(3):235-274, July 2010 (article)

Abstract
A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most stable". In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Multi-Label Learning by Exploiting Label Dependency

Zhang, M., Zhang, K.

In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), pages: 999-1008, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

Abstract
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the condi- tional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other well-established methods.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A Maximum Entropy Approach to Semi-supervised Learning

Erkan, A., Altun, Y.

30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), 30, pages: 80, July 2010 (poster)

Abstract
Maximum entropy (MaxEnt) framework has been studied extensively in supervised learning. Here, the goal is to find a distribution p that maximizes an entropy function while enforcing data constraints so that the expected values of some (pre-defined) features with respect to p match their empirical counterparts approximately. Using different entropy measures, different model spaces for p and different approximation criteria for the data constraints yields a family of discriminative supervised learning methods (e.g., logistic regression, conditional random fields, least squares and boosting). This framework is known as the generalized maximum entropy framework. Semi-supervised learning (SSL) has emerged in the last decade as a promising field that combines unlabeled data along with labeled data so as to increase the accuracy and robustness of inference algorithms. However, most SSL algorithms to date have had trade-offs, e.g., in terms of scalability or applicability to multi-categorical data. We extend the generalized MaxEnt framework to develop a family of novel SSL algorithms. Extensive empirical evaluation on benchmark data sets that are widely used in the literature demonstrates the validity and competitiveness of the proposed algorithms.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Efficient Filter Flow for Space-Variant Multiframe Blind Deconvolution

Hirsch, M., Sra, S., Schölkopf, B., Harmeling, S.

In Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition, pages: 607-614, IEEE, Piscataway, NJ, USA, CVPR, June 2010 (inproceedings)

Abstract
Ultimately being motivated by facilitating space-variant blind deconvolution, we present a class of linear transformations, that are expressive enough for space-variant filters, but at the same time especially designed for efficient matrix-vector-multiplications. Successful results on astronomical imaging through atmospheric turbulences and on noisy magnetic resonance images of constantly moving objects demonstrate the practical significance of our approach.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Grasping with Vision Descriptors and Motor Primitives

Kroemer, O., Detry, R., Piater, J., Peters, J.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2010), pages: 47-54, (Editors: Filipe, J. , J. Andrade-Cetto, J.-L. Ferrier), SciTePress , Lisboa, Portugal, 7th International Conference on Informatics in Control, Automation and Robotics (ICINCO), June 2010 (inproceedings)

Abstract
Grasping is one of the most important abilities needed for future service robots. Given the task of picking up an object from betweem clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which may not always be available. Therefore, methods for executing grasps are required, which perform well with information gathered from only standard stereo vision, and make only a few necessary assumptions about the task environment. We propose techniques that reactively modify the robot’s learned motor primitives based on information derived from Early Cognitive Vision descriptors. The proposed techniques employ non-parametric potential fields centered on the Early Cognitive Vision descriptors to allow for curving hand trajectories around objects, and finger motions that adapt to the object’s local geometry. The methods were tested on a real robot and found to allow for easier imitation learning of human movements and give a considerable improvement to the robot’s performance in grasping tasks.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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An efficient divide-and-conquer cascade for nonlinear object detection

Lampert, CH.

In Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pages: 1022-1029, IEEE, Piscataway, NJ, USA, Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010 (inproceedings)

Abstract
We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search (ESS) procedure [11] can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernelized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50% by our method compared to standard cascade evaluation.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Non-parametric estimation of integral probability metrics

Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B., Lanckriet, G.

In Proceedings of the IEEE International Symposium on Information Theory (ISIT 2010), pages: 1428-1432, IEEE, Piscataway, NJ, USA, IEEE International Symposium on Information Theory (ISIT), June 2010 (inproceedings)

Abstract
In this paper, we develop and analyze a nonparametric method for estimating the class of integral probability metrics (IPMs), examples of which include the Wasserstein distance, Dudley metric, and maximum mean discrepancy (MMD). We show that these distances can be estimated efficiently by solving a linear program in the case of Wasserstein distance and Dudley metric, while MMD is computable in a closed form. All these estimators are shown to be strongly consistent and their convergence rates are analyzed. Based on these results, we show that IPMs are simple to estimate and the estimators exhibit good convergence behavior compared to fi-divergence estimators.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Causal Markov condition for submodular information measures

Steudel, B., Janzing, D., Schölkopf, B.

In Proceedings of the 23rd Annual Conference on Learning Theory, pages: 464-476, (Editors: AT Kalai and M Mohri), OmniPress, Madison, WI, USA, COLT, June 2010 (inproceedings)

Abstract
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences among the observations that are entailed by a causal hypothesis in terms of a directed acyclic graph. In the conventional setting, the observations are random variables and the independence is a statistical one, i.e., the information content of observations is measured in terms of Shannon entropy. We formulate a generalized CMC for any kind of observations on which independence is defined via an arbitrary submodular information measure. Recently, this has been discussed for observations in terms of binary strings where information is understood in the sense of Kolmogorov complexity. Our approach enables us to find computable alternatives to Kolmogorov complexity, e.g., the length of a text after applying existing data compression schemes. We show that our CMC is justified if one restricts the attention to a class of causal mechanisms that is adapted to the respective information measure. Our justification is similar to deriving the statistical CMC from functional models of causality, where every variable is a deterministic function of its observed causes and an unobserved noise term. Our experiments on real data demonstrate the performance of compression based causal inference.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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The effect of positioning aids on PET quantification following MR-based attenuation correction (AC) in PET/MR imaging

Mantlik, F., Hofmann, M., Kupferschläger, J., Werner, M., Pichler, B., Beyer, T.

Journal of Nuclear Medicine, 51(Supplement 2):1418 , June 2010 (poster)

Abstract
Objectives: We study the quantitative effect of not accounting for the attenuation of patient positioning aids in combined PET/MR imaging. Methods: Positioning aids cannot be detected with conventional MR sequences. We mimic this effect using PET/CT data (Biograph HiRez16) with the foams removed from CT images prior to using them for CT-AC. PET/CT data were acquired using standard parameters (phantoms/patients): 120/140 kVp, 30/250 mAs, 5 mm slices, OSEM (4i, 8s, 5 mm filter) following CT-AC. First, a uniform 68Ge-cylinder was positioned centrally in the PET/CT and fixed with a vacuum mattress (10 cm thick). Second, the same cylinder was placed in 3 positioning aids from the PET/MR (BrainPET-3T). Third, 5 head/neck patients who were fixed in a vacuum mattress were selected. In all 3 studies PET recon post CT-AC based on measured CT images was used as the reference (mCT-AC). The PET/MR set-up was mimicked by segmenting the foam inserts from the measured CT images and setting their voxel values to -1000 HU (air). PET images were reconstructed using CT-AC with the segmented CT images (sCT-AC). PET images with mCT- and sCT-AC were compared. Results: sCT-AC underestimated PET voxel values in the phantom by 6.7% on average compared to mCT-AC with the vacuum mattress in place. 5% of the PET voxels were underestimated by >=10%. Not accounting for MR positioning aids during AC led to an underestimation of 2.8% following sCT-AC, with 5% of the PET voxels being underestimated by >=7% wrt mCT-AC. Preliminary evaluation of the patient data indicates a slightly higher bias from not accounting for patient positioning aids (mean: -9.1%, 5% percentile: -11.2%). Conclusions: A considerable and regionally variable underestimation of the PET activity following AC is observed when positioning aids are not accounted for. This bias may become relevant in neurological activation or dementia studies with PET/MR

ei

Web [BibTex]

Web [BibTex]


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UDP Communication channel design of master-slave robot system

Hong, A., Cho, JH., Wang, H., Lee, DY.

In pages: 231-232, 2010 KSME Conference, June 2010 (inproceedings)

ei

[BibTex]

[BibTex]


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Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory

Janzing, D., Steudel, B.

Open Systems and Information Dynamics, 17(2):189-212, June 2010 (article)

Abstract
A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X for just two observed variables X and Y. It is based on the observation that there exist (non-Gaussian) joint distributions P(X,Y) for which Y may be written as a function of X up to an additive noise term that is independent of X and no such model exists from Y to X. Whenever this is the case, one prefers the causal model X → Y. Here we justify this method by showing that the causal hypothesis Y → X is unlikely because it requires a specific tuning between P(Y) and P(X|Y) to generate a distribution that admits an additive noise model from X to Y. To quantify the amount of tuning, needed we derive lower bounds on the algorithmic information shared by P(Y) and P(X|Y). This way, our justification is consistent with recent approaches for using algorithmic information theory for causal reasoning. We extend this principle to the case where P(X,Y) almost admits an additive noise model. Our results suggest that the above conclusion is more reliable if the complexity of P(Y) is high.

ei

PDF Web DOI [BibTex]


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Telling cause from effect based on high-dimensional observations

Janzing, D., Hoyer, P., Schölkopf, B.

In Proceedings of the 27th International Conference on Machine Learning, pages: 479-486, (Editors: J Fürnkranz and T Joachims), International Machine Learning Society, Madison, WI, USA, ICML, June 2010 (inproceedings)

Abstract
We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if the covariance matrix of the cause and the structure matrix mapping the cause to the effect are independently chosen. The method applies to both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Multi-task Learning for Zero Training Brain-Computer Interfaces

Alamgir, M., Grosse-Wentrup, M., Altun, Y.

4th International BCI Meeting, June 2010 (poster)

Abstract
Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subject-specific calibration data prior to actual use of the BCI for communication. In this work, we utilize the framework of multitask learning to construct a BCI that can be used without any subject-specific calibration process, i.e., with zero training data. In BCIs based on EEG or MEG, the predictive function of a subject's intention is commonly modeled as a linear combination of some features derived from spatial and spectral recordings. The coefficients of this combination correspond to the importance of the features for predicting the intention of the subject. These coefficients are usually learned separately for each subject due to inter-subject variability. Principle feature characteristics, however, are known to remain invariant across subject. For example, it is well known that in motor imagery paradigms spectral power in the mu- and beta frequency ranges (roughly 8-14 Hz and 20-30 Hz, respectively) over sensorimotor areas provides most information on a subject's intention. Based on this assumption, we define the intention prediction function as a combination of subject-invariant and subject-specific models, and propose a machine learning method that infers these models jointly using data from multiple subjects. This framework leads to an out-of-the-box intention predictor, where the subject-invariant model can be employed immediately for a subject with no prior data. We present a computationally efficient method to further improve this BCI to incorporate subject-specific variations as such data becomes available. To overcome the problem of high dimensional feature spaces in this context, we further present a new method for finding the relevance of different recording channels according to actions performed by subjects. Usually, the BCI feature representation is a concatenation of spectral features extracted from different channels. This representation, however, is redundant, as recording channels at different spatial locations typically measure overlapping sources within the brain due to volume conduction. We address this problem by assuming that the relevance of different spectral bands is invariant across channels, while learning different weights for each recording electrode. This framework allows us to significantly reduce the feature space dimensionality without discarding potentially useful information. Furthermore, the resulting out-of-the-box BCI can be adapted to different experimental setups, for example EEG caps with different numbers of channels, as long as there exists a mapping across channels in different setups. We demonstrate the feasibility of our approach on a set of experimental EEG data recorded during a standard two-class motor imagery paradigm from a total of ten healthy subjects. Specifically, we show that satisfactory classification results can be achieved with zero training data, and that combining prior recordings with subject-specific calibration data substantially outperforms using subject-specific data only.

ei

Web [BibTex]


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Causal Influence of Gamma Oscillations on Performance in Brain-Computer Interfaces

Grosse-Wentrup, M., Hill, J., Schölkopf, B.

4th International BCI Meeting0, June 2010 (poster)

Abstract
Background and Objective: While machine learning approaches have led to tremendous advances in brain-computer interfaces (BCIs) in recent years (cf. [1]), there still exists a large variation in performance across subjects. Furthermore, a significant proportion of subjects appears incapable of achieving above chance-level classification accuracy [2], which to date includes all subjects in a completely locked-in state that have been trained in BCI control. Understanding the reasons for this variation in performance arguably constitutes one of the most fundamental open questions in research on BCIs. Methods & Results Using a machine learning approach, we derive a trial-wise measure of how well EEG recordings can be classified as either left- or right-hand motor imagery. Specifically, we train a support vector machine (SVM) on log-bandpower features (7-40 Hz) derived from EEG channels after spatial filtering with a surface Laplacian, and then compute the trial-wise distance of the output of the SVM from the separating hyperplane using a cross-validation procedure. We then correlate this trial-wise performance measure, computed on EEG recordings of ten healthy subjects, with log-bandpower in the gamma frequency range (55-85 Hz), and demonstrate that it is positively correlated with frontal- and occipital gamma-power and negatively correlated with centro-parietal gamma-power. This correlation is shown to be highly significant on the group level as well as in six out of ten subjects on the single-subject level. We then utilize the framework for causal inference developed by Pearl, Spirtes and others [3,4] to present evidence that gamma-power is not only correlated with BCI performance but does indeed exert a causal influence on it. Discussion and Conclusions Our results indicate that successful execution of motor imagery, and hence reliable communication by means of a BCI based on motor imagery, requires a volitional shift of gamma-power from centro-parietal to frontal and occipital regions. As such, our results provide the first non-trivial explanation for the variation in BCI performance across and within subjects. As this topographical alteration in gamma-power is likely to correspond to a specific attentional shift, we propose to provide subjects with feedback on their topographical distribution of gamma-power in order to establish the attentional state required for successful execution of motor imagery.

ei

Web [BibTex]


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Solving large-scale nonnegative least-squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS 2010), 16, pages: 19, June 2010, based on Joint work with Dongmin Kim and Inderjit Dhillon (poster)

Abstract
We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Hanson [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by exploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established convex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Dynamic Dissimilarity Measure for Support-Based Clustering

Lee, D., Lee, J.

IEEE Transactions on Knowledge and Data Engineering, 22(6):900-905, June 2010 (article)

Abstract
Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Sparse Spectrum Gaussian Process Regression

Lázaro-Gredilla, M., Quiñonero-Candela, J., Rasmussen, CE., Figueiras-Vidal, AR.

Journal of Machine Learning Research, 11, pages: 1865-1881, June 2010 (article)

Abstract
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations. We discuss both the weight space and function space representations, and note that the new construction implies priors over functions which are always stationary, and can approximate any covariance function in this class.

ei

PDF [BibTex]

PDF [BibTex]


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A scalable trust-region algorithm with application to mixed-norm regression

Kim, D., Sra, S., Dhillon, I.

In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pages: 519-526, (Editors: Fürnkranz, J. , T. Joachims), International Machine Learning Society, Madison, WI, USA, 27th International Conference on Machine Learning (ICML), June 2010 (inproceedings)

Abstract
We present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably, for sparsity-promoting regularizers such as ℓ1 or ℓ1, ∞ norms, it enables efficient computation of sparse solutions. Our approach is based on the trust-region framework with nonsmooth objectives, which allows us to build on known results to provide convergence analysis. We avoid the computational overheads associated with the conventional Hessian approximation used by trust-region methods by instead using a simple separable quadratic approximation. This approximation also enables use of proximity operators for tackling nonsmooth regularizers. We illustrate the versatility of our resulting algorithm by specializing it to three mixed-norm regression problems: group lasso [36], group logistic regression [21], and multi-task lasso [19]. We experiment with both synthetic and real-world large-scale data—our method is seen to be competitive, robust, and scalable.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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The Influence of the Image Basis on Modeling and Steganalysis Performance

Schwamberger, V., Le, P., Schölkopf, B., Franz, M.

In Information Hiding, pages: 133-144, (Editors: R Böhme and PWL Fong and R Safavi-Naini), Springer, Berlin, Germany, 12th international Workshop (IH), June 2010 (inproceedings)

Abstract
We compare two image bases with respect to their capabilities for image modeling and steganalysis. The first basis consists of wavelets, the second is a Laplacian pyramid. Both bases are used to decompose the image into subbands where the local dependency structure is modeled with a linear Bayesian estimator. Similar to existing approaches, the image model is used to predict coefficient values from their neighborhoods, and the final classification step uses statistical descriptors of the residual. Our findings are counter-intuitive on first sight: Although Laplacian pyramids have better image modeling capabilities than wavelets, steganalysis based on wavelets is much more successful. We present a number of experiments that suggest possible explanations for this result.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Simultaneous PET/MRI for the evaluation of hemato-oncological diseases with lower extremity manifestations

Sauter, A., Horger, M., Boss, A., Kolb, A., Mantlik, F., Kanz, L., Pfannenberg, C., Stegger, L., Claussen, C., Pichler, B.

Journal of Nuclear Medicine, 51(Supplement 2):1001 , June 2010 (poster)

Abstract
Objectives: The study purpose is the evaluation of patients, suffering from hemato-oncological disease with complications at the lower extremities, using simultaneous PET/MRI. Methods: Until now two patients (chronic active graft-versus-host-disease [GvHD], B-non Hodgkin lymphoma [B-NHL]) before and after therapy were examined in a 3-Tesla-BrainPET/MRI hybrid system following F-18-FDG-PET/CT. Simultaneous static PET (1200 sec.) and MRI scans (T1WI, T2WI, post-CA) were acquired. Results: Initial results show the feasibility of using hybrid PET/MRI-technology for musculoskeletal imaging of the lower extremities. Simultaneous PET and MRI could be acquired in diagnostic quality. Before treatment our patient with GvHD had a high fascia and muscle FDG uptake, possibly due to muscle encasement. T2WI and post gadolinium T1WI revealed a fascial thickening and signs of inflammation. After therapy with steroids followed by imatinib the patient’s symptoms improved while, the muscular FDG uptake droped whereas the MRI signal remained unchanged. We assume that fascial elasticity improved during therapy despite persistance of fascial thickening. The examination of the second patient with B-NHL manifestation in the tibia showed a significant signal and uptake decrease in the bone marrow and surrounding lesions in both, MRI and PET after therapy with rituximab. The lack of residual FDG-uptake proved superior to MRI information alone helping for exclusion of vital tumor. Conclusions: Combined PET/MRI is a powerful tool to monitor diseases requiring high soft tissue contrast along with molecular information from the FDG uptake.

ei

Web [BibTex]

Web [BibTex]


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Unsupervised Object Discovery: A Comparison

Tuytelaars, T., Lampert, CH., Blaschko, MB., Buntine, W.

International Journal of Computer Vision, 88(2):284-302, June 2010 (article)

Abstract
The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A PAC-Bayesian Analysis of Co-clustering, Graph Clustering, and Pairwise Clustering

Seldin, Y.

In ICML 2010 Workshop on Social Analytics: Learning from human interactions, pages: 1-5, ICML Workshop on Social Analytics: Learning from human interactions, June 2010 (inproceedings)

Abstract
We review briefly the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008, 2009, 2010), which provided generalization guarantees and regularization terms absent in the preceding formulations of this problem and achieved state-of-the-art prediction results in MovieLens collaborative filtering task. Inspired by this analysis we formulate weighted graph clustering1 as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. Following the lines of (Seldin and Tishby, 2010) we derive PAC-Bayesian generalization bounds for graph clustering. The bounds show that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering a more accurate way to deal with finite sample issues.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Solving Large-Scale Nonnegative Least Squares

Sra, S.

16th Conference of the International Linear Algebra Society (ILAS), June 2010 (talk)

Abstract
We study the fundamental problem of nonnegative least squares. This problem was apparently introduced by Lawson and Hanson [1] under the name NNLS. As is evident from its name, NNLS seeks least-squares solutions that are also nonnegative. Owing to its wide-applicability numerous algorithms have been derived for NNLS, beginning from the active-set approach of Lawson and Han- son [1] leading up to the sophisticated interior-point method of Bellavia et al. [2]. We present a new algorithm for NNLS that combines projected subgradients with the non-monotonic gradient descent idea of Barzilai and Borwein [3]. Our resulting algorithm is called BBSG, and we guarantee its convergence by ex- ploiting properties of NNLS in conjunction with projected subgradients. BBSG is surprisingly simple and scales well to large problems. We substantiate our claims by empirically evaluating BBSG and comparing it with established con- vex solvers and specialized NNLS algorithms. The numerical results suggest that BBSG is a practical method for solving large-scale NNLS problems.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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How to Explain Individual Classification Decisions

Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Müller, K.

Journal of Machine Learning Research, 11, pages: 1803-1831, June 2010 (article)

Abstract
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]