Filtered gaussian processes for learning with large data-sets

Shi, J.Q., Murray-Smith, R., Titterington, D.M. and Pearlmutter, B.A. (2005) Filtered gaussian processes for learning with large data-sets. Lecture Notes in Computer Science, 3355, pp. 128-139. (doi: 10.1007/b105497)



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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.

Item Type:Articles
Keywords:Filtering transformation, gaussian process regression model, Karhunen-Loeve expansion, kernel-based non-parametric models, principal componenet analysis.
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Shi, J.Q., Murray-Smith, R., Titterington, D.M., and Pearlmutter, B.A.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Copyright Holders:Copyright © 2005 Springer
First Published:First published in Lecture Notes in Computer Science 3355:128-139
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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