A unifying view of sparse approximate Gaussian process regression

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A unifying view of sparse approximate Gaussian process regression (2005)

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by Joaquin Quiñonero-candela , Carl Edward Rasmussen , Ralf Herbrich

Venue:	Journal of Machine Learning Research
Citations:	155 - 6 self

BibTeX

@ARTICLE{Quiñonero-candela05aunifying,
    author = {Joaquin Quiñonero-candela and Carl Edward Rasmussen and Ralf Herbrich},
    title = {A unifying view of sparse approximate Gaussian process regression},
    journal = {Journal of Machine Learning Research},
    year = {2005},
    volume = {6},
    pages = {2005}
}

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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.

Keyphrases

sparse approximate gaussian process regression unifying view new insight corresponding full gps known approximation justified ranking approach relies gaussian process regression new unifying view attractive computational constraint proper probabilistic sparse approximation sparse approximation effective prior

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