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    Filtered Gaussian Processes for Learning with Large Data-Sets

    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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    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
    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:
    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
    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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