Folded-concave penalization approaches to tensor completion | |
Cao WF(曹文飞); Wang Y(王尧); Yang, Can; Chang, Xiangyu; Han Z(韩志)![]() | |
Department | 机器人学研究室 |
Source Publication | Neurocomputing
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ISSN | 0925-2312 |
2015 | |
Volume | 152Pages:261-273 |
Indexed By | SCI ; EI |
EI Accession number | 20150400440904 |
WOS ID | WOS:000349572600027 |
Contribution Rank | 3 |
Funding Organization | National 973 Project of China under Grant number 2013 CB329404 and Natural Science Foundation of China under Grantnumbers 61273020and61303168. |
Keyword | Tensor Completion Nuclear Norm Folded-concave Penalization Local Linear Approximation Sparse Learning |
Abstract | The existing studies involving matrix or tensor completion problems are commonly under the nuclear norm penalization framework due to the computational efficiency of the resulting convex optimization problem. Folded-concave penalization methods have demonstrated surprising developments in sparse learning problems due to their nice practical and theoretical properties. To share the same light of folded-concave penalization methods, we propose a new tensor completion model via folded-concave penalty for estimating missing values in tensor data. Two typical folded-concave penalties, the minmax concave plus (MCP) penalty and the smoothly clipped absolute deviation (SCAD) penalty, are employed in the new model. To solve the resulting nonconvex optimization problem, we develop a local linear approximation augmented Lagrange multiplier (LLA-ALM) algorithm which combines a two-step LLA strategy to search a local optimum of the proposed model efficiently. Finally, we provide numerical experiments with phase transitions, synthetic data sets, real image and video data sets to exhibit the superiority of the proposed model over the nuclear norm penalization method in terms of the accuracy and robustness. |
Language | 英语 |
WOS Headings | Science & Technology ; Technology |
WOS Subject | Computer Science, Artificial Intelligence |
WOS Keyword | Variable Selection ; Oracle Properties ; Regularization ; Algorithms ; Matrices ; Lasso ; Model |
WOS Research Area | Computer Science |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.sia.cn/handle/173321/15660 |
Collection | 机器人学研究室 |
Corresponding Author | Wang Y(王尧) |
Affiliation | 1.School of Mathematics and Statistics, Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, China 2.Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
Recommended Citation GB/T 7714 | Cao WF,Wang Y,Yang, Can,et al. Folded-concave penalization approaches to tensor completion[J]. Neurocomputing,2015,152:261-273. |
APA | Cao WF,Wang Y,Yang, Can,Chang, Xiangyu,Han Z,&Xu ZB.(2015).Folded-concave penalization approaches to tensor completion.Neurocomputing,152,261-273. |
MLA | Cao WF,et al."Folded-concave penalization approaches to tensor completion".Neurocomputing 152(2015):261-273. |
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