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A Subspace Kernel for Nonlinear Feature Extraction

2007

Conference Paper

ei


Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used pre-processing step in pattern classification and data mining tasks. Given a positive definite kernel function, it is well known that the input data are implicitly mapped to a feature space with usually very high dimensionality. The goal of KFE is to find a low dimensional subspace of this feature space, which retains most of the information needed for classification or data analysis. In this paper, we propose a subspace kernel based on which the feature extraction problem is transformed to a kernel parameter learning problem. The key observation is that when projecting data into a low dimensional subspace of the feature space, the parameters that are used for describing this subspace can be regarded as the parameters of the kernel function between the projected data. Therefore current kernel parameter learning methods can be adapted to optimize this parameterized kernel function. Experimental results are provided to validate the effectiveness of the proposed approach.

Author(s): Wu, M. and Farquhar, J.
Book Title: IJCAI-07
Journal: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)
Pages: 1125-1130
Year: 2007
Month: January
Day: 0
Editors: Veloso, M. M.
Publisher: AAAI Press

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

Event Name: International Joint Conference on Artificial Intelligence
Event Place: Hyderabad, India

Address: Menlo Park, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4159,
  title = {A Subspace Kernel for Nonlinear Feature Extraction},
  author = {Wu, M. and Farquhar, J.},
  journal = {Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)},
  booktitle = {IJCAI-07},
  pages = {1125-1130},
  editors = {Veloso, M. M.},
  publisher = {AAAI Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Menlo Park, CA, USA},
  month = jan,
  year = {2007},
  month_numeric = {1}
}