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Self-centralized jointly sparse maximum margin criterion for robust dimensionality reduction

文献类型：期刊论文

作者Hu, Liangchen1; Xu, Jingke3; Tian, Lei2,4; Zhang, Wensheng1,2
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2020-10-28
卷号206页码:15
关键词Maximum margin criterion Robustness Adaptive centroid L-1,L-2-norm sparsity Dimensionality reduction
ISSN号0950-7051
DOI10.1016/j.knosys.2020.106343
通讯作者Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要Linear discriminant analysis (LDA) is among the most popular supervised dimensionality reduction algorithms, which has been largely followed in the fields of pattern recognition and data mining. However, LDA has three major drawbacks. One is the challenge brought by small-sample-size (SSS) problem; second makes it sensitive to outliers due to the use of squared L-2-norms in the scatter loss evaluation; the third is the case that the feature loadings in projection matrix are relatively redundant and there is a risk of overfitting. In this paper, we put forward a novel functional expression for LDA, which combines maximum margin criterion (MMC) with a weighted strategy formulated by L-1,L-2-norms to against outliers. Meanwhile, we simultaneously realize the adaptive calculation of weighted intra-class and global centroid to further reduce the influence of outliers, and employ the L-2,L-1-norm to constrain row sparsity so that subspace learning and feature selection could be performed cooperatively. Besides, an effective alternating iterative algorithm is derived and its convergence is verified. From the complexity analysis, our proposed algorithm can deal with large-scale data processing. Our proposed model can address the sensitivity problem of outliers and extract the most representative features while preventing overfitting effectively. Experiments performed on several benchmark databases demonstrate that the proposed algorithm is more effective than some other state-of-the-art methods and has better generalization performance. (C) 2020 Elsevier B.V. All rights reserved.
WOS关键词LINEAR DISCRIMINANT-ANALYSIS ; FACE RECOGNITION
资助项目National Key R&D Program of China[2017YFC0806500] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[61772525]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000572851100006
出版者ELSEVIER
资助机构National Key R&D Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/42022]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhang, Wensheng
作者单位1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
3.Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China
4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Liangchen,Xu, Jingke,Tian, Lei,et al. Self-centralized jointly sparse maximum margin criterion for robust dimensionality reduction[J]. KNOWLEDGE-BASED SYSTEMS,2020,206:15.
APA Hu, Liangchen,Xu, Jingke,Tian, Lei,&Zhang, Wensheng.(2020).Self-centralized jointly sparse maximum margin criterion for robust dimensionality reduction.KNOWLEDGE-BASED SYSTEMS,206,15.
MLA Hu, Liangchen,et al."Self-centralized jointly sparse maximum margin criterion for robust dimensionality reduction".KNOWLEDGE-BASED SYSTEMS 206(2020):15.

入库方式： OAI收割

来源：自动化研究所

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