Lossy compression of observations for Gaussian process regression

Abstract

This paper proposes a novel approach of Gaussian process observation set compression based on a squared difference measure. It is used to discard observations to speed up Gaussian process prediction while retaining the information encoded in the full set of observations. Furthermore, this paper compares the regression performance of a compressed Gaussian process to its uncompressed version and to a randomly downsampled Gaussian process for a standard two-dimensional test function. The empirical results of this paper show that this is an effective algorithm for Gaussian process compression, speeding up prediction while maintaining predictive accuracy with regards to the predicted means.