Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss

doi:10.1109/TNNLS.2018.2852297

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss
	Peng, Hanyang1,2,3 ; Liu, Cheng-Lin2,3,4
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2019-03-01
卷号	30 期号:3 页码:788-802
通讯作者	Liu, Cheng-Lin(liucl@nlpria.ac.cn)
摘要	A wide variety of sparsity-inducing feature selection methods have been developed in recent years. Most of the loss functions of these approaches are built upon regression since it is general and easy to optimize, but regression is not well suitable for classification. In contrast, the hinge loss (HL) of support vector machines has proved to be powerful to handle classification tasks, but a model with existing multiclass HL and sparsity regularization is difficult to optimize. In view of that, we propose a new loss, called smooth and robust HL, which gathers the merits of regression and HL but overcome their drawbacks, and apply it to our sparsity regularized feature selection model. To optimize the model, we present a new variant of accelerated proximal gradient (APG) algorithm, which boosts the discriminative margins among different classes, compared with standard APG algorithms. We further propose an efficient optimization technique to solve the proximal projection problem at each iteration step, which is a key component of the new APG algorithm. We theoretically prove that the new APG algorithm converges at rate O(1/k(2)) if it is convex (k is the iteration counter), which is the optimal convergence rate for smooth problems. Experimental results on nine publicly available data sets demonstrate the effectiveness of our method.
关键词	Accelerated proximal gradient (APG) extended hinge loss (HL) feature selection sparsity regularization
DOI	10.1109/TNNLS.2018.2852297
关键词[WOS]	LEAST-SQUARES REGRESSION ; SUPPORT VECTOR MACHINES ; CLASSIFICATION ; INFORMATION ; ALGORITHMS ; FRAMEWORK ; SHRINKAGE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61721004]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000459536100013
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	模式识别基础
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25029
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Liu, Cheng-Lin
作者单位	1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Peng, Hanyang,Liu, Cheng-Lin. Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(3):788-802.
APA	Peng, Hanyang,&Liu, Cheng-Lin.(2019).Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(3),788-802.
MLA	Peng, Hanyang,et al."Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.3(2019):788-802.