Fast Gaussian process regression using representative data

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Gaussian process regression is a Bayesian nonparametric regression model. Although the Gaussian process regression has shown good performance in various experiments, it suffers from O(N/sup 3/) computational cost, where N is the number of training data. We propose a method using representative data for the Gaussian process regression. The representative data are modified so that the regression model fits the original training data. The proposed method requires O(NM/sup 2/) computational cost, where M(<N) is the number of the representative data. According to our experiments, the results of the proposed method are comparable to those of the original method, although it requires only much smaller number of the representative data than the number of the original training data.

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