Dimensionality reduction based on sparse representation and nonparametric discriminant analysis
Jingjing Zhao · Journal of National University of Defense Technology · 2013
Aiming at the face recognition problem,a new supervised dimensionality reduction algorithm is presented.On the basis of sparse representation theory,the proposed algorithm uses the within-class sparse construction to construct graph.This scheme can avoid the difficulty of parameter selection in traditional graph construction methods,and characterize the within-class information well.Furthermore,the multi-class nonparametric discriminant scatter is applied to characterize the between-class information,which will be more discriminative than parametric discriminant scatter in dealing with complex-distributed data.By maximizing the nonparametric between-class scatter and preserving the within-class sparse reconstructive relationship,the proposed algorithm can seek for the optimal projection matrix.Experimental results on ORL and Extended Yale B dataset show that the proposed method can achieve good recognition effect.