A Fast Implementation of PCA-L1 Using Gram-Schmidt Orthogonalization

Mariko HIROKAWA  Yoshimitsu KUROKI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.3   pp.559-561
Publication Date: 2013/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.559
Print ISSN: 0916-8532
Type of Manuscript: Special Section LETTER (Special Section on Face Perception and Recognition)
Category: Face Perception and Recognition
Keyword: 
principal component analysis based on L1-norm maximization,  Gram-Schmidt orthogonalization,  

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Summary: 
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.