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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Publisher's URL: http://dx.doi.org/10.1007/b105497
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 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 |
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Keywords: | Filtering transformation, gaussian process regression model, Karhunen-Loeve expansion, kernel-based non-parametric models, principal componenet analysis. |
Status: | Published |
Refereed: | Yes |
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 |
Publisher: | Springer |
ISSN: | 1611-3349 |
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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