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A Unifying View of Sparse Approximate Gaussian Process Regression




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.

Author(s): Quinonero Candela, J. and Rasmussen, CE.
Journal: Journal of Machine Learning Research
Volume: 6
Pages: 1935-1959
Year: 2005
Month: December
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {A Unifying View of Sparse Approximate Gaussian Process Regression},
  author = {Quinonero Candela, J. and Rasmussen, CE.},
  journal = {Journal of Machine Learning Research},
  volume = {6},
  pages = {1935-1959},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2005},
  month_numeric = {12}