Fast sparse gaussian processes learning for man-made structure classification

Abstract

Informative Vector Machine (IVM) is an efficient fast sparse Gaussian process's (GP) method previously suggested for active learning. It greatly reduces the computational cost of GP classification and makes the GP learning close to real time. We apply IVM for man-made structure classification (a two class problem). Our work includes the investigation of the performance of IVM with varied active data points as well as the effects of different choices of GP kernels. Satisfactory results have been obtained, showing that the approach keeps full GP classification performance and yet is significantly faster (by virtue if using a subset of the whole training data points).