A CONVERGENT SOLUTION TO TWO DIMENSIONAL LINEAR DISCRIMINANT ANALYSIS

被引：0
|
作者：
Chen, Wei [1 ]
Huang, Kaiqi [1 ]
Tan, Tieniu [1 ]
Tao, Dacheng [2 ]
机构：
[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
[2] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore
来源：
2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6 | 2009年
基金：
中国国家自然科学基金;
关键词：
Evolutionary computation; 2DLDA; convergence; subspace learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training stage. This paper presents an evolutionary computation based solution, referred to as E-2DLDA, to provide a convergent training stage for 2DLDA. In E-2DLDA, every randomly generated candidate projection matrices are first normalized. The evolutionary computation method optimizes the projection matrices to best separate different classes. Experimental results show E-2DLDA is convergent and outperforms 2DLDA.
引用
收藏
页码：4133 / +
页数：3
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