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Sparse on-line gaussian processes
| Venue: | Neural Computation |
| Citations: | 183 - 8 self |
BibTeX
@ARTICLE{Csató_sparseon-line,
author = {Lehel Csató},
title = {Sparse on-line gaussian processes},
journal = {Neural Computation},
year = {}
}
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Abstract
Minor corrections included a a The authors acknowledge reader feedbacks 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 for 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. Sparse Online Gaussian Processes 2
Keyphrases
sparse on-line gaussian process large data set sparse gaussian approximation gaussian process projection technique posterior process sequential construction bayesian error measure reader feedback bayesian online sparse online gaussian effective parameter sparse representation relevant subsample rkhs norm minor correction kernel machine gp model bayesian type
