A Fast Implementation of PCA-L1 Using Gram-Schmidt Orthogonalization
Mariko Hirokawa, Yoshimitsu Kuroki · IEICE Transactions on Information and Systems · 2013
PCA-L1 (principal component analysis based on L1-norm maximization) is an approximate solution of L1-PCA (PCA based on the L1-norm), and has robustness against outliers compared with traditional PCA. However, the more dimensions the feature space has, the more calculation time PCA-L1 consumes. This paper focuses on an initialization procedure of PCA-L1 algorithm, and proposes a fast method of PCA-L1 using Gram-Schmidt orthogonalization. Experimental results on face recognition show that the proposed method works faster than conventional PCA-L1 without decrease of recognition accuracy.