Conference Publication Details
Mandatory Fields
Shi J.;Murray-Smith R.;Titterington D.;Pearlmutter B.
Lecture Notes in Computer Science
Filtered Gaussian processes for learning with large data-sets
Optional Fields
Filtering transformation Gaussian process regression model Karhunen-Loeve expansion Kernel-based non-parametric models Principal component analysis
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. © Springer-Verlag Berlin Heidelberg 2005.
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