Online local learning algorithms for linear discriminant analysis

被引：16

作者：

Demir, GK ^{[1
]}

Omehmet, K

机构：

[1] Dokuz Eylul Univ, Dept Elect & Elect Engn, TR-35160 Izmir, Turkey

[2] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA

来源：

PATTERN RECOGNITION LETTERS | 2005年 / 26卷 / 04期

关键词：

linear discriminant analysis; generalized eigenvalue problem; online local learning;

D O I：

10.1016/j.patrec.2004.08.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Online local learning algorithms for a laterally-connected single-layer neural network for performing linear discriminant analysis have been proposed. A convergence proof is provided for the algorithm based on Hebbian learning. The algorithms are simulated and applied to the face recognition problem. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：421 / 431

页数：11

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