Fast greedy insertion and deletion in sparse Gaussian process regression.

摘要

In this paper, we introduce a new and straightforward criterion for successive insertion and deletion of training points in sparse Gaussian process regression. Our novel approach is based on an approximation of the selection technique proposed by Smola and Bartlett (1). It is shown that the resulting selection strategies are as fast as the purely randomized schemes for insertion and deletion of training points. Experiments on real-world robot data demonstrate that our obtained regression models are competitive with the computationally intensive state-of-the-art methods in terms of generalization accuracy.