Fast Gaussian process regression using representative data
Taku Yoshioka, Shin Ishii · 2002
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(