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Self-Weighted Supervised Discriminative Feature Selection
Zhang, Rui1,2; Nie, Feiping1,2; Li, Xuelong3
Department光学影像学习与分析中心
2018-08
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume29Issue:8Pages:3913-3918
Contribution Rank3
Abstract

In this brief, a novel self-weighted orthogonal linear discriminant analysis (SOLDA) problem is proposed, and a self-weighted supervised discriminative feature selection (SSD-FS) method is derived by introducing sparsity-inducing regularization to the proposed SOLDA problem. By using the row-sparse projection, the proposed SSD-FS method is superior to multiple sparse feature selection approaches, which can overly suppress the nonzero rows such that the associated features are insufficient for selection. More specifically, the orthogonal constraint ensures the minimal number of selectable features for the proposed SSD-FS method. In addition, the proposed feature selection method is able to harness the discriminant power such that the discriminative features are selected. Consequently, the effectiveness of the proposed SSD-FS method is validated theoretically and experimentally.

KeywordRow-sparse Projection Self-weighted Orthogonal Linear Discriminant Analysis (Solda) Sparsity-inducing Regularization Supervised Feature Selection
Subject AreaComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
DOI10.1109/TNNLS.2017.2740341
Indexed BySCI ; EI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS IDWOS:000439627700052
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20183105639566
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30535
Collection光学影像学习与分析中心
Corresponding AuthorNie, Feiping
Affiliation1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
2.Northwestern Polytech Univ, Ctr Opt Imagery Anal & Learning, Xian 710072, Shaanxi, Peoples R China
3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Rui,Nie, Feiping,Li, Xuelong. Self-Weighted Supervised Discriminative Feature Selection[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(8):3913-3918.
APA Zhang, Rui,Nie, Feiping,&Li, Xuelong.(2018).Self-Weighted Supervised Discriminative Feature Selection.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(8),3913-3918.
MLA Zhang, Rui,et al."Self-Weighted Supervised Discriminative Feature Selection".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.8(2018):3913-3918.
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