Max residual classifier

Abstract

We introduce a novel classifier, called max residual classifier (MRC), for learning a sparse representation jointly with a discriminative decision function. MRC seeks to maximize the differences between the residual errors of the wrong classes and the right one. This effectively leads to a more discriminative sparse representation and better classification accuracy. The optimization procedure is simple and efficient. Its objective function is closely related to the decision function of the residual classification strategy. Unlike existing methods for learning discriminative sparse representation that are restricted to a linear model, our approach is able to work with a non-linear model via the use of Mercer kernel. Experimental results show that MRC is able to capture meaningful and compact structures of data. Its performances compare favourably with the current state of the art on challenging benchmarks including rotated MNIST, Caltech-101, Caltech-256, and SHREC'11 non-rigid 3D shapes.