L1-norm-based 2DPCA

Authors:
Xuelong Li;Yanwei Pang;Yuan Yuan
Affiliations:
State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China;School of Electronic Information Engineering, Tianjin University, Tianjin, China;School of Engineering and Applied Science, Aston University, Birmingham, UK
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Year:
2010

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Abstract

In this paper, we first present a simple but effective L1-norm-based two-dimensional principal component analysis (2DPCA). Traditional L2-norm-based least squares criterion is sensitive to outliers, while the newly proposed L1-norm 2DPCA is robust. Experimental results demonstrate its advantages.