Multiclass linear dimension reduction by weighted pairwise Fisher criteria

被引：357

作者：

Loog, M

Duin, RPW

Haeb-Umbach, R

机构：

[1] Univ Utrecht, Med Ctr, Image Sci Inst, NL-3508 GA Utrecht, Netherlands

[2] Delft Univ Technol, Dept Appl Phys, Pattern Recognit Grp, NL-2600 GA Delft, Netherlands

[3] Philips Res Labs, D-52066 Aachen, Germany

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2001年 / 23卷 / 07期

关键词：

linear dimension reduction; Fisher criterion; linear discriminant analysis; Bayes error; approximate pairwise accuracy criterion;

D O I：

10.1109/34.935849

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known It-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidian distance of the respective class means. We generalize upon LDA by introducing a different weighting function.

引用

页码：762 / 766

页数：5

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