L1-Norm-Based 2DPCA

被引：249

作者：

Li, Xuelong ^{[1
]}

Pang, Yanwei ^{[2
]}

Yuan, Yuan ^{[3
]}

机构：

[1] Chinese Acad Sci, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China

[2] Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China

[3] Aston Univ, Sch Engn & Appl Sci, Birmingham B4 7ET, W Midlands, England

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2010年 / 40卷 / 04期

基金：

中国国家自然科学基金;

关键词：

L1; norm; outlier; subspace; two-dimensional principal component analysis (2DPCA); PRINCIPAL COMPONENT ANALYSIS; FACE; RECOGNITION; REPRESENTATION; APPEARANCE; PCA;

D O I：

10.1109/TSMCB.2009.2035629

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1170 / 1175

页数：6

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