Sparse Online Gaussian Processes
Sparse Online Gaussian Processes
We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations 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 GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments.
641-668
Csato, Lehel
840e5a56-3991-4aff-8cc6-a51cb56e7d00
Opper, Manfred
f7f8690a-fdcb-46f0-857d-c4140648039b
2002
Csato, Lehel
840e5a56-3991-4aff-8cc6-a51cb56e7d00
Opper, Manfred
f7f8690a-fdcb-46f0-857d-c4140648039b
Abstract
We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations 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 GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments.
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Published date: 2002
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 259182
URI: http://eprints.soton.ac.uk/id/eprint/259182
PURE UUID: 0adcda9f-3758-4a15-9129-3170fbe0be5d
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Date deposited: 15 Mar 2004
Last modified: 14 Mar 2024 06:20
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Contributors
Author:
Lehel Csato
Author:
Manfred Opper
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