Greedy Gaussian Process Regression Applied to Object Categorization and Regression

Abstract

In this work we propose an approximation of Gaussian Process and apply it to Classification and Regression tasks. We, primarily, target the problem of visual object categorization using a Greedy variant of Gaussian Processes. To deal with the prohibitive training and inferencing cost of GP, we devise a greedy approach to subset selection and the inducing input choice to approximate the kernel matrix, resulting in faster retrieval timings. A localized combination of kernel functions is designed and used in a framework of sparse approximations to Gaussian Processes for visual object categorization and generic regression tasks. Through exhaustive experimentation and empirical results we demonstrate the effectiveness of the proposed approach, when compared with other kernel based methods. © 2018 ACM.