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A Unifying View of Sparse Approximate Gaussian Process Regression

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83845

Quinonero Candela,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Quinonero Candela, J., & Rasmussen, C. (2005). A Unifying View of Sparse Approximate Gaussian Process Regression. Journal of Machine Learning Research, 6, 1935-1959. Retrieved from http://jmlr.csail.mit.edu/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D361-3
Zusammenfassung
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.