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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
作者部门机器人学研究室
关键词high-dimensional data sparsity compressive sensing low-rank matrix recovery tensors
发表期刊National Science Review
ISSN2095-5138
2018
卷号5期号:5页码:756-767
收录类别EI
EI收录号20184205963574
产权排序1
摘要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.
语种英语
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/23424
专题机器人学研究室
通讯作者Wang Y(王尧); Meng DY(孟德宇); Yuan, Ming
作者单位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
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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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