PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximations for Binary Gaussian Process Classification
Hannes Nickisch and Carl Edward Rasmussen
Journal of Machine Learning Research Volume 9, pp. 2035-2078, 2008. ISSN 1532-4435


We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results. We examine both 1) the quality of the predictive distributions and 2) the suitability of the different marginal likelihood approximations for model selection (selecting hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods produce good predictive distributions although their marginal likelihood approximations are poor. Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost always the method of choice unless the computational budget is very tight. We also extend existing methods in various ways, and provide unifying code implementing all approaches.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5312
Deposited By:Carl Edward Rasmussen
Deposited On:24 March 2009