Feature selection based on kernel discriminant analysis

被引：0
|
作者：
Ashihara, Masamichi [1 ]
Abe, Shigeo [1 ]
机构：
[1] Kobe Univ, Grad Sch Sci & Technol, Kobe, Hyogo, Japan
来源：
ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2 | 2006年 / 4132卷
关键词：
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：
For two-class problems we propose two feature selection criteria based on kernel discriminant analysis. The first one is the objective function of kernel discriminant analysis (KDA) and the second one is the KDA-based exception ratio. We show that the objective function of KDA is monotonic for the deletion of features, which ensures stable feature selection. The KDA-based exception ratio defines the overlap between classes in the one-dimensional space obtained by KDA. The computer experiments show that the both criteria work well to select features but the former is more stable.
引用
收藏
页码：282 / 291
页数：10
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