Analysis of Some Methods for Reduced Rank Gaussian Process Regression

2005

Conference Paper

ei


While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning the covariance function hyperparameters and the support set. We propose a method for learning hyperparameters for a given support set. We also review the Sparse Greedy GP (SGGP) approximation (Smola and Bartlett, 2001), which is a way of learning the support set for given hyperparameters based on approximating the posterior. We propose an alternative method to the SGGP that has better generalization capabilities. Finally we make experiments to compare the different ways of training a RRGP. We provide some Matlab code for learning RRGPs.

Author(s): Quinonero Candela, J. and Rasmussen, CE.
Book Title: Switching and Learning in Feedback Systems
Pages: 98-127
Year: 2005
Day: 0
Editors: Murray Smith, R. , R. Shorten
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Berlin, Germany
Digital: 0
DOI: 10.1007/978-3-540-30560-6_4
Event Name: European Summer School on Multi-Agent Control 2003
Event Place: Maynooth, Ireland
ISBN: 978-3-540-24457-8
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
PDF

BibTex

@inproceedings{2745,
  title = {Analysis of Some Methods for Reduced Rank Gaussian Process Regression},
  author = {Quinonero Candela, J. and Rasmussen, CE.},
  booktitle = {Switching and Learning in Feedback Systems},
  pages = {98-127},
  editors = {Murray Smith, R. , R. Shorten},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  year = {2005}
}