Dimension reduction in nonparametric kernel discriminant analysis

被引：9
|
作者：
Hernández, A
Velilla, S
机构：
[1] Univ Exeter, Dept Math Sci, Exeter EX4 4QE, Devon, England
[2] Univ Carlos III Madrid, Dept Estadist, E-28903 Getafe, Spain
来源：
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | 2005年 / 14卷 / 04期
关键词：
class separation; curse of dimensionality; dimension-reduction subspaces; Kernel discriminant rule; projection pursuit; sliced average variance estimation (SAVE); sliced inverse regression (SIR);
D O I：
10.1198/106186005X79610
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：
This article develops a dimension-reduction method in kernel discriminant analysis, based on a general concept of separation of populations. The ideas we present lead to a characterization of the central subspace that does not impose restrictions on the marginal distribution of the feature vector. We also give a new procedure for estimating relevant directions in the central subspace. Comparisons to other procedures are studied and examples of application are discussed.
引用
收藏
页码：847 / 866
页数：20
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