PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Unifying View of Sparse Approximate Gaussian Process Regression
Joaquin Quinonero Candela and Carl Edward Rasmussen
Journal of Machine Learning Reserch Volume 6, pp. 1935-1959, 2005.

Abstract

We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2632
Deposited By:Carl Edward Rasmussen
Deposited On:22 November 2006