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Sparse recovery: From vectors to tensors
Wang Y(王尧)1,2; Meng DY(孟德宇)1,3; Yuan, Ming4
Department机器人学研究室
Source PublicationNational Science Review
ISSN2095-5138
2018
Volume5Issue:5Pages:756-767
Indexed BySCI ; EI ; CSCD
EI Accession number20184205963574
WOS IDWOS:000448667000025
CSCD IDCSCD:6384517
Contribution Rank1
KeywordHigh-dimensional Data Sparsity Compressive Sensing Low-rank Matrix Recovery Tensors
Abstract

Recent advances in various fields such as telecommunications, biomedicine and economics, among others, have created enormous amount of data that are often characterized by their huge size and high dimensionality. It has become evident, from research in the past couple of decades, that sparsity is a flexible and powerful notion when dealing with these data, both from empirical and theoretical viewpoints. In this survey, we review some of the most popular techniques to exploit sparsity, for analyzing high-dimensional vectors, matrices and higher-order tensors.

Language英语
WOS SubjectMultidisciplinary Sciences
WOS KeywordRESTRICTED ISOMETRY PROPERTY ; COHERENT TIGHT FRAMES ; SIGNAL RECOVERY ; UNCERTAINTY PRINCIPLES ; L-1/2 REGULARIZATION ; VARIABLE SELECTION ; RANK ; REPRESENTATION ; RECONSTRUCTION ; CONSTRUCTIONS
WOS Research AreaScience & Technology - Other Topics
Funding ProjectNational Natural Science Foundation of China[11501440] ; National Natural Science Foundation of China[61373114] ; National Natural Science Foundation of China[61273020] ; National Natural Science Foundation of China[61661166011] ; National Natural Science Foundation of China[61721002] ; National Basic Research Program of China (973 Program)[2013CB329404] ; National Science Foundation[DMS-1265202]
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23424
Collection机器人学研究室
Corresponding AuthorWang Y(王尧); Meng DY(孟德宇); Yuan, Ming
Affiliation1.School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 10016, China
3.Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China
4.Department of Statistics, Columbia University, New York, NY 10027, United States
Recommended Citation
GB/T 7714
Wang Y,Meng DY,Yuan, Ming. Sparse recovery: From vectors to tensors[J]. National Science Review,2018,5(5):756-767.
APA Wang Y,Meng DY,&Yuan, Ming.(2018).Sparse recovery: From vectors to tensors.National Science Review,5(5),756-767.
MLA Wang Y,et al."Sparse recovery: From vectors to tensors".National Science Review 5.5(2018):756-767.
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