Sparse representation for Gaussian process models

Lehel Csató; Manfred Opper

Sparse representation for Gaussian process models

Lehel Csató, Manfred Opper

Aston University

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	444-450
Number of pages	7
Journal	Advances in Neural Information Processing Systems
Volume	13
Publication status	Published - 2002
Event	Advances in Neural Information Processing Systems 1994 - Singapore, Singapore Duration: 16 Nov 1994 → 18 Nov 1994

Bibliographical note

Availble on Google books

Keywords

sparse representation
Gaussian Process
limitations
large data sets
Bayesian online algorithm
subsample

Cite this

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title = "Sparse representation for Gaussian process models",

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.",

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AB - 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.

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KW - limitations

KW - large data sets

KW - Bayesian online algorithm

KW - subsample

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JF - Advances in Neural Information Processing Systems

T2 - Advances in Neural Information Processing Systems 1994

Y2 - 16 November 1994 through 18 November 1994

ER -

Sparse representation for Gaussian process models

Abstract

Bibliographical note

Keywords

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Cite this