Bayesian site selection for fast Gaussian process regression

Arash Pourhabib, Faming Liang, Yu Ding

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Gaussian Process (GP) regression is a popular method in the field of machine learning and computer experiment designs; however, its ability to handle large data sets is hindered by the computational difficulty in inverting a large covariance matrix. Likelihood approximation methods were developed as a fast GP approximation, thereby reducing the computation cost of GP regression by utilizing a much smaller set of unobserved latent variables called pseudo points. This article reports a further improvement to the likelihood approximation methods by simultaneously deciding both the number and locations of the pseudo points. The proposed approach is a Bayesian site selection method where both the number and locations of the pseudo inputs are parameters in the model, and the Bayesian model is solved using a reversible jump Markov chain Monte Carlo technique. Through a number of simulated and real data sets, it is demonstrated that with appropriate priors chosen, the Bayesian site selection method can produce a good balance between computation time and prediction accuracy: it is fast enough to handle large data sets that a full GP is unable to handle, and it improves, quite often remarkably, the prediction accuracy, compared with the existing likelihood approximations. © 2014 Taylor and Francis Group, LLC.
Original languageEnglish (US)
Pages (from-to)543-555
Number of pages13
JournalIIE Transactions
Issue number5
StatePublished - Feb 5 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: Arash Pourhabib and Yu Ding were supported in part by NSF grants CMMI-0926803 and CMMI-1000088; Yu Ding was also supported by the NSF grant CMMI-0726939; Faming Liang's research was partially supported by NSF grants CMMI-0926803, DMS-1007457, and DMS-1106494 and an award (KUS-C1-016-04) made by King Abdullah University of Science and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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