Fast Gaussian process posteriors with product trees
David A. Moore, Stuart Russell · 2014
Gaussian processes (GP) are a powerful tool for nonparametric regression; unfortunately, calcu-lating the posterior variance in a standard GP model requires time O(n2) in the size of the training set. Previous work by Shen et al. (2006) used a k-d tree structure to approximate the pos-terior mean in certain GP models. We extend this approach to achieve efficient approximation of the posterior covariance using a tree clustering on pairs of training points, and demonstrate sig-nificant improvements in performance with neg-ligible loss of accuracy. 1