Supervised independent component analysis by maximizing relative entropy

被引：0
|
作者：
Huang, YP [1 ]
Luo, SW [1 ]
Qi, YJ [1 ]
机构：
[1] Beijing Jiaotong Univ, Dept Comp Sci & Technol, Beijing 100044, Peoples R China
来源：
2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3 | 2004年
关键词：
independent component analysis; face recognition; iris recognition;
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：
This paper presents a novel algorithm called SICA-MRE (Supervised Independent Component Analysis by Maxinlizing Relative Entropy). It removes the disadvantage in traditional ICA. which ignores the contributions of independent components to recognition performance. Experimental results in face and iris recognition show that the presented algorithm has better performance.
引用
收藏
页码：1593 / 1596
页数：4
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