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  A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., & Rasmussen, C. (2005). A Unifying View of Sparse Approximate Gaussian Process Regression. The Journal of Machine Learning Research, 6, 1935-1959.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D361-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D736-D
Genre: Journal Article

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 Creators:
Quinonero Candela, J1, 2, Author              
Rasmussen, CE1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

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 Dates: 2005-12
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3753
 Degree: -

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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 6 Sequence Number: - Start / End Page: 1935 - 1959 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1