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Group feature selection with multiclass support vector machine
Tang FZ(唐凤珍)1; Adam, Lukas2; Si BL(斯白露)1
Department机器人学研究室
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2018-11-23
Volume317Pages:42-49
Indexed BySCI ; EI
EI Accession number20183605788718
WOS IDWOS:000444237900004
Contribution Rank1
Funding OrganizationState Key Laboratory of Robotics ; Distinguished Young Scholar Project of the Thousand Talents Program of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China
KeywordGroup Feature Selection Support Vector Machine Multiclass Support Vector Machine Alternating Direction Method Of Multipliers Eeg Channel Selection
Abstract

Feature reduction is nowadays an important topic in machine learning as it reduces the complexity of the final model and makes it easier to interpret. In some applications, the features arise from multiple sources and it is not so important to select the individual features as to select the important sources. This leads to a group feature selection problem. In this paper, we consider the group feature selection in the multiclass classification setting based on the framework of support vector machines. We reformulate it as a sparse problem by prescribing the maximum number of active groups and propose a novel method based on the ADMM algorithm. We proposed the method in such a way that the main computational load is performed in the first iteration and the remaining iterations can be computed fast. This allows us to handle large problems. We demonstrate the good performance of our method on several real-world datasets. (C) 2018 Elsevier B.V. All rights reserved.

Language英语
WOS SubjectComputer Science, Artificial Intelligence
WOS KeywordOptimality Conditions ; Pattern-recognition ; Sparse ; Bci
WOS Research AreaComputer Science
Funding ProjectState Key Laboratory of Robotics[Y7C120E101] ; Distinguished Young Scholar Project of the Thousand Talents Program of China[Y5A1370101] ; Ministry of Science and Technology of China[2017YFC0804003] ; National Natural Science Foundation of China[61329302]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22776
Collection机器人学研究室
Corresponding AuthorAdam, Lukas
Affiliation1.State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences,Shenyang, Liaoning Province 110016, China
2.Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, China
Recommended Citation
GB/T 7714
Tang FZ,Adam, Lukas,Si BL. Group feature selection with multiclass support vector machine[J]. NEUROCOMPUTING,2018,317:42-49.
APA Tang FZ,Adam, Lukas,&Si BL.(2018).Group feature selection with multiclass support vector machine.NEUROCOMPUTING,317,42-49.
MLA Tang FZ,et al."Group feature selection with multiclass support vector machine".NEUROCOMPUTING 317(2018):42-49.
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