An efficient algorithm to solve the small sample size problem for LDA

被引：53

作者：

Zheng, WM ^{[1
]}

Zhao, L ^{[1
]}

Zou, CR ^{[1
]}

机构：

[1] SE Univ, Engn Res Ctr Informat Proc & Applicat, Jiangsu 210096, Peoples R China

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 05期

关键词：

linear discriminant analysis; null space; face recognition;

D O I：

10.1016/j.patcog.2003.02.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present an efficient algorithm to solve the most discriminant vectors of LDA for high-dimensional data set. The experiments on ORL face database confirm the effectiveness of the proposed method. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1077 / 1079

页数：3

共 5 条

[1] A new LDA-based face recognition system which can solve the small sample size problem
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[2] Face recognition based on the uncorrelated discriminant transformation
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Hu, ZS
Lou, Z
[J]. PATTERN RECOGNITION, 2001, 34 (07) : 1405 - 1416
[3] Ke Liu, 1992, International Journal of Pattern Recognition and Artificial Intelligence, V6, P817, DOI 10.1142/S0218001492000412
[4] [束婷婷 Shu Tingting], 2002, [南京理工大学学报. 自然科学版, Journal of nanjing university of science and technology], V26, P290
[5] A direct LDA algorithm for high-dimensional data - with application to face recognition
Yu, H
Yang, H
[J]. PATTERN RECOGNITION, 2001, 34 (10) : 2067 - 2070

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