基于L1范数的二维主成分分析
L1-norm-based 2DPCA.
作者信息
Li Xuelong, Pang Yanwei, Yuan Yuan
机构信息
State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China.
出版信息
IEEE Trans Syst Man Cybern B Cybern. 2010 Aug;40(4):1170-5. doi: 10.1109/TSMCB.2009.2035629. Epub 2010 Jan 15.
PMID:20083461
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.
摘要
在本文中，我们首先提出一种简单但有效的基于L1范数的二维主成分分析（2DPCA）。传统的基于L2范数的最小二乘准则对异常值敏感，而新提出的基于L1范数的2DPCA具有鲁棒性。实验结果证明了它的优势。
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