期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 40, 期 4, 页码 1170-1175出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2009.2035629
关键词
L1 norm; outlier; subspace; two-dimensional principal component analysis (2DPCA)
类别
资金
- National Natural Science Foundation of China [60605005, 60975001]
- Chinese Academy of Sciences
- State Key Laboratory of CADCG [A0902]
- Zhejiang University
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据
分享论文
