PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hierarchical Bayesian Modelling with Gaussian Processes
Anton Schwaighofer, Volker Tresp and Kai Yu
In: Neural Information Processing Systems 2004, 13-16 Dec 2004, Vancouver, Canada.

Abstract

We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystroem method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:465
Deposited By:Anton Schwaighofer
Deposited On:23 December 2004