A new linear discriminant analysis algorithm based on L1-norm maximization and locality preserving projection

被引：7

作者：

Zhang, Di ^{[1
]}

Li, Xueqiang ^{[1
]}

He, Jiazhong ^{[2
]}

Du, Minghui ^{[3
]}

机构：

[1] Guangdong Med Univ, Sch Informat Engn, Dongguan, Peoples R China

[2] Shaoguan Univ, Dept Phys, Shaoguan, Peoples R China

[3] South China Univ Technol, Sch Elect & Informat, Guangzhou, Guangdong, Peoples R China

来源：

PATTERN ANALYSIS AND APPLICATIONS | 2018年 / 21卷 / 03期

基金：

中国博士后科学基金;

关键词：

Feature extraction; Linear discriminant analysis; Local information; L1-norm; L2-norm; Pattern classification; DIMENSIONALITY REDUCTION; FEATURE-EXTRACTION; GENETIC ALGORITHM; FACE; FRAMEWORK; LDA;

D O I：

10.1007/s10044-017-0594-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Generic L2-norm-based linear discriminant analysis (LDA) is sensitive to outliers and only captures global structure information of sample points. In this paper, a new LDA-based feature extraction algorithm is proposed to integrate both global and local structure information via a unified L1-norm optimization framework. Unlike generic L2-norm-based LDA, the proposed algorithm explicitly incorporates the local structure information of sample points and is robust to outliers. It overcomes the problem of the singularity of within-class scatter matrix as well. Experiments on several popular datasets demonstrate the effectiveness of the proposed algorithm.

引用

页码：685 / 701

页数：17

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