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

doi:10.1016/j.knosys.2020.106343

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	Self-centralized jointly sparse maximum margin criterion for robust dimensionality reduction
	Hu, Liangchen 1; Xu, Jingke 3; Tian, Lei 2,4; Zhang, Wensheng 1,4
刊名	KNOWLEDGE-BASED SYSTEMS
	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
DOI	10.1016/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.
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关键词	LINEAR DISCRIMINANT-ANALYSIS ; FACE RECOGNITION
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000572851100006
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
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.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China 4.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, 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.