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Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss

文献类型：期刊论文

作者Peng, Hanyang1,2,3; Liu, Cheng-Lin2,3,4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2019-03-01
卷号30期号:3页码:788-802
关键词Accelerated proximal gradient (APG) extended hinge loss (HL) feature selection sparsity regularization
ISSN号2162-237X
DOI10.1109/TNNLS.2018.2852297
通讯作者Liu, Cheng-Lin(liucl@nlpria.ac.cn)
英文摘要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.
WOS关键词LEAST-SQUARES REGRESSION ; SUPPORT VECTOR MACHINES ; CLASSIFICATION ; INFORMATION ; ALGORITHMS ; FRAMEWORK ; SHRINKAGE
资助项目National Natural Science Foundation of China[61721004]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000459536100013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/25028]  
专题自动化研究所_模式识别国家重点实验室_模式分析与学习团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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