Shi, Jian Qing and Murray-Smith, Roderick and Titterington, D. Mike and Pearlmutter, Barak A. (2005) Filtered Gaussian Processes for Learning with Large Data-Sets. Lecture Notes in Computer Science (3355). pp. 128-139. ISSN 0302-9743

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Abstract

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 smalldimensional 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:	Article
Additional Information:	Proceedings of Switching and Learning in Feedback Systems: European Summer School on Multi-Agent Control, Maynooth, Ireland, September 8-10 2003. The original publication is available at www.springerlink.com
Keywords:	Filtering transformation, Gaussian process regression model, Karhunen-Loeve expansion; Kernel-based non-parametric models; Principal component analysis;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	2511
Identification Number:	https://doi.org/10.1007/978-3-540-30560-6_5
Depositing User:	Hamilton Editor
Date Deposited:	27 Apr 2011 14:56
Journal or Publication Title:	Lecture Notes in Computer Science
Publisher:	Springer Verlag
Refereed:	Yes
URI:	http://www.springerlink.com/
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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