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 Publication | National Science Review
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ISSN | 2095-5138 |
2018 | |
Volume | 5Issue:5Pages:756-767 |
Indexed By | SCI ; EI ; CSCD |
EI Accession number | 20184205963574 |
WOS ID | WOS:000448667000025 |
CSCD ID | CSCD:6384517 |
Contribution Rank | 1 |
Keyword | High-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 Subject | Multidisciplinary Sciences |
WOS Keyword | RESTRICTED ISOMETRY PROPERTY ; COHERENT TIGHT FRAMES ; SIGNAL RECOVERY ; UNCERTAINTY PRINCIPLES ; L-1/2 REGULARIZATION ; VARIABLE SELECTION ; RANK ; REPRESENTATION ; RECONSTRUCTION ; CONSTRUCTIONS |
WOS Research Area | Science & Technology - Other Topics |
Funding Project | National 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] ; National 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 | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/23424 |
Collection | 机器人学研究室 |
Corresponding Author | Wang Y(王尧); Meng DY(孟德宇); Yuan, Ming |
Affiliation | 1.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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Sparse recovery_ Fro(1871KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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