A matching pursuit approach to sparse Gaussian process regression

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Sathiya Keerthi, Wei Chu


In this paper we propose a new basis selection criterion for building sparse GP regression models that provides promising gains in accuracy as well as efficiency over previous methods. Our algorithm is much faster than that of Smola and Bartlett, while, in generalization it greatly outperforms the information gain approach proposed by Seeger et al, especially on the quality of predictive distributions.