Learning with large data sets using filtered Gaussian Process priors
J. Q. Shi, Roderick Murray-Smith, Mike Titterington and B. A. Pearlmutter
Proceedings of the Hamilton Summer School on Switching and Learning in Feedback systems
Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large data-sets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a small-dimensional set of filtered data that keeps a high proportion of the information contained in the original large data-set. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically.
Keywords: Filtering transformation, Gaussian process regression model, Karhunen-Loeve expansion, Kernel-based non-parametric models, Principal component analysis.