Identification of Influential Cases in Kernel Fisher Discriminant Analysis

被引：1

作者：

Louw, Nelmarie ^{[1
]}

Lamont, Morne M. C. ^{[1
]}

Steel, Sarel J. ^{[1
]}

机构：

[1] Univ Stellenbosch, Dept Stat & Actuarial Sci, ZA-7602 Matieland, South Africa

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2008年 / 37卷 / 10期

关键词：

Atypical cases; Classification; Error rate; Kernel methods;

D O I：

10.1080/03610910802278859

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：

We study the influence of a single data case on the results of a statistical analysis. This problem has been addressed in several articles for linear discriminant analysis (LDA). Kernel Fisher discriminant analysis (KFDA) is a kernel based extension of LDA. In this article, we study the effect of atypical data points on KFDA and develop criteria for identification of cases having a detrimental effect on the classification performance of the KFDA classifier. We find that the criteria are successful in identifying cases whose omission from the training data prior to obtaining the KFDA classifier results in reduced error rates.

引用

页码：2050 / 2062

页数：13

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