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
收藏
页码：685 / 701
页数：17
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