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A Fast Implementation of PCA-L1 Using Gram-Schmidt Orthogonalization
Mariko HIROKAWA Yoshimitsu KUROKI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2013/03/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: Special Section LETTER (Special Section on Face Perception and Recognition)
Category: Face Perception and Recognition
principal component analysis based on L1-norm maximization, Gram-Schmidt orthogonalization,
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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.