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Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
Hou, Chenping1; Nie, Feiping2; Li, Xuelong3; Yi, Dongyun1; Wu, Yi1
Department光学影像学习与分析中心
2014-06-01
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
Volume44Issue:6Pages:793-804
AbstractFeature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the l(2,1)-norm regularization, and design an effective algorithm to solve the corresponding optimization problem. Furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. In all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. Promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.
SubtypeArticle
KeywordEmbedding Learning Feature Selection Pattern Recognition Sparse Regression
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCYB.2013.2272642
Indexed BySCI ; EI
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION ; FEATURE-EXTRACTION ; SPACE ; MODEL
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000337960000006
Citation statistics
Cited Times:184[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/22361
Collection光学影像学习与分析中心
Affiliation1.Natl Univ Def Technol, Dept Math & Syst Sci, Changsha 410073, Hunan, Peoples R China
2.Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA
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
Hou, Chenping,Nie, Feiping,Li, Xuelong,et al. Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(6):793-804.
APA Hou, Chenping,Nie, Feiping,Li, Xuelong,Yi, Dongyun,&Wu, Yi.(2014).Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection.IEEE TRANSACTIONS ON CYBERNETICS,44(6),793-804.
MLA Hou, Chenping,et al."Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection".IEEE TRANSACTIONS ON CYBERNETICS 44.6(2014):793-804.
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