CPGL: A classification method combining PCA and the Group Lasso method

Sparse representation based optimization has emerged as a new paradigm for solving classification problems and has achieved satisfactory performances. Recent research, however, has revealed its noteworthy limitation in handling samples with high intra-class pair-wise correlations. In this paper, we study this problem from a novel perspective of de-correlating the input data. A new method is proposed by combining Principle Component Analysis (PCA) and the Group Lasso method. The highly correlated training samples are first orthogonalized using PCA, and then the Group Lasso algorithm is adopted for performing the classification. Experimental results show that our proposed method over-performs the Group Lasso method in the face recognition application on two public databases.