Dual subspace discriminative projection learning

Abstract

In this paper, we propose a dual subspace discriminative projection learning (DSDPL) framework for multi-category image classification. Our approach reflects the notion that images are composed of class-shared information, class-specific information, and sparse noise. Unlike traditional subspace learning methods, DSDPL serves to decompose original high dimensional data, via learned projection matrices, into class-shared and class-specific subspaces. The learned projection matrices are jointly constrained with l2,1 sparse norm and LDA terms while the reconstructive properties of DSDPL reduce information loss, leading to greater stability within low dimensional subspaces. Regression-based terms are also included to facilitate a more robust classification approach, using extracted class-specific features for better classification. Our approach is examined on five different datasets for face, object and scene classifications. Experimental results demonstrate not only the superiority and versatility of DSDPL over current benchmark approaches, but also a more robust classification approach with low sample size training data.