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Feature Selection Based on Structured Sparsity: A Comprehensive Study
Gui, Jie1; Sun, Zhenan2; Ji, Shuiwang3; Tao, Dacheng4; Tan, Tieniu2
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2017-07-01
Volume28Issue:7Pages:1490-1507
SubtypeArticle
AbstractFeature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have been widely studied in the last few years, and a number of algorithms have been proposed. However, there is no comprehensive study concerning the connections between different SSFS methods, and how they have evolved. In this paper, we attempt to provide a survey on various SSFS methods, including their motivations and mathematical representations. We then explore the relationship among different formulations and propose a taxonomy to elucidate their evolution. We group the existing SSFS methods into two categories, i.e., vector-based feature selection (feature selection based on lasso) and matrix-based feature selection (feature selection based on l(r, p)-norm). Furthermore, FS has been combined with other machine learning algorithms for specific applications, such as multitask learning, multilabel learning, multiview learning, classification, and clustering. This paper not only compares the differences and commonalities of these methods based on regression and regularization strategies, but also provides useful guidelines to practitioners working in related fields to guide them how to do feature selection.
KeywordDimensionality Reduction Feature Selection Sparse Structured Sparsity
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2551724
WOS KeywordSUPPORT VECTOR MACHINES ; UNSUPERVISED FEATURE-SELECTION ; ROBUST FEATURE-EXTRACTION ; MAXIMUM MARGIN CRITERION ; DIMENSIONALITY REDUCTION ; VARIABLE SELECTION ; GENE SELECTION ; LOGISTIC-REGRESSION ; IMAGING GENETICS ; CLASSIFICATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61572463) ; "Thirteenth Five-Year" National Key Research and Development Program of China(2016YFD0702002) ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)(201700027) ; Open Project Program of the State Key Lab of CADCG(A1709) ; Zhejiang University ; Shanghai Key Laboratory of Intelligent Information Processing, China(IIPL-2016-003) ; Australian Research Council(DP-140102164 ; U.S. National Science Foundation(DBI-1147134 ; FT-130101457 ; DBI-1350258) ; LE140100061)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000404048300001
Citation statistics
Cited Times:72[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15236
Collection智能感知与计算研究中心
Affiliation1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA
4.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia
Recommended Citation
GB/T 7714
Gui, Jie,Sun, Zhenan,Ji, Shuiwang,et al. Feature Selection Based on Structured Sparsity: A Comprehensive Study[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(7):1490-1507.
APA Gui, Jie,Sun, Zhenan,Ji, Shuiwang,Tao, Dacheng,&Tan, Tieniu.(2017).Feature Selection Based on Structured Sparsity: A Comprehensive Study.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(7),1490-1507.
MLA Gui, Jie,et al."Feature Selection Based on Structured Sparsity: A Comprehensive Study".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.7(2017):1490-1507.
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