Robust 2DLDA based on correntropy

被引：6
作者：
Zhong, Fujin [1 ,2 ]
Liu, Li [1 ,2 ]
Hu, Jun [1 ,2 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
来源：
NEUROCOMPUTING | 2018年 / 316卷
基金：
中国国家自然科学基金;
关键词：
2DLDA; Outliers; Robustness; Correntropy; Optimization; LINEAR DISCRIMINANT-ANALYSIS;
D O I：
10.1016/j.neucom.2018.08.026
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To further improve the robustness of two-dimensional LDA (2DLDA) methods against outliers, this paper proposes a new robust 2DLDA version which obtains the optimal projection transformation by maximizing the correntropy-based within-class similarity and maintaining the global dispersity simultaneously. The objective problem of the proposed method can be solved by an iterative optimization algorithm which is proved to converge at a local maximum point. The experimental results on FERET face database, PolyU palmprint database and Binary Alphadigits database illustrate that the proposed method outperforms three conventional 2DLDA methods when there are outliers. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：399 / 404
页数：6
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