Sparse representation for Gaussian process models (2001)
by
Lehel Csató
,
Manfred Opper
| Venue: | Advances in Neural Information Processing Systems |
| Citations: | 41 - 1 self |
BibTeX
@INPROCEEDINGS{Csató01sparserepresentation,
author = {Lehel Csató and Manfred Opper},
title = {Sparse representation for Gaussian process models},
booktitle = {Advances in Neural Information Processing Systems },
year = {2001},
pages = {444--450},
publisher = {MIT Press}
}
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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.
