A PCA Based Unsupervised Feature Selection Algorithm

被引：5
|
作者：
Luo, Yihui [1 ]
Xiong, Shuchu [1 ]
Wang, Sichuan [1 ]
机构：
[1] Hunan Univ Commerce, Dept Informat, Changsha, Hunan, Peoples R China
来源：
SECOND INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING: WGEC 2008, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/WGEC.2008.109
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Principal components analysis (PCA) is an important approach to unsupervised dimensionality reduction. However, principal components (PCs) are a set of new variables carrying no clear physical meanings and still require all the original variables. To deal with this problem, the PC dominant feature (PCDF) is defined. Then, feature selection using them is considered and a new algorithm for determining such PC dominant features is proposed. Experimental results show that using the principal components as the basis the new algorithm can find a good feature subset.
引用
收藏
页码：299 / 302
页数：4
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