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.
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Shi, J.Q., Murray-Smith, R., Titterington, D.M., Pearlmutter, B.A. (2005). Filtered Gaussian Processes for Learning with Large Data-Sets. In: Murray-Smith, R., Shorten, R. (eds) Switching and Learning in Feedback Systems. Lecture Notes in Computer Science, vol 3355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30560-6_5
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DOI: https://doi.org/10.1007/978-3-540-30560-6_5
Publisher Name: Springer, Berlin, Heidelberg
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