KAUST Grant NumberKUS-C1-016-04
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AbstractGaussian 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.
CitationPourhabib A, Liang F, Ding Y (2014) Bayesian site selection for fast Gaussian process regression. IIE Transactions 46: 543–555. Available: http://dx.doi.org/10.1080/0740817X.2013.849833.
SponsorsArash 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.
PublisherInforma UK Limited