Sparse representation for Gaussian process models

Csató, Lehel and Opper, Manfred (2002). Sparse representation for Gaussian process models. Advances in Neural Information Processing Systems, 13 , pp. 444-450.

Abstract

We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experimental results on toy examples and large real-world datasets indicate the efficiency of the approach.

Divisions: Aston University (General)
Additional Information: Availble on Google books
Uncontrolled Keywords: sparse representation,Gaussian Process,limitations,large data sets,Bayesian online algorithm,subsample
Full Text Link: http://mitpress ... type=2&tid=8662
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Published Date: 2002
Authors: Csató, Lehel
Opper, Manfred

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