Arrow Diagrams for Kernel Principal Component Analysis

被引：3
|
作者：
Huh, Myung-Hoe [1 ]
机构：
[1] Korea Univ, Dept Stat, Anam Dong 5-1, Seoul 136701, South Korea
来源：
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS | 2013年 / 20卷 / 03期
关键词：
Principal component analysis; kernel method; radial basis function; biplot; arrow diagram;
D O I：
10.5351/CSAM.2013.20.3.175
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.
引用
收藏
页码：175 / 184
页数：10
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