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Folded-concave penalization approaches to tensor completion
Cao, Wenfei; Wang, Yao; Yang, Can; Chang, Xiangyu; Han Z(韩志); Xu, Zongben
作者部门机器人学研究室
关键词Tensor Completion Nuclear Norm Folded-concave Penalization Local Linear Approximation Sparse Learning
发表期刊Neurocomputing
ISSN0925-2312
2015
卷号152页码:261-273
收录类别SCI ; EI
EI收录号20150400440904
WOS记录号WOS:000349572600027
产权排序3
资助机构National 973 Project of China under Grant number 2013 CB329404 and Natural Science Foundation of China under Grantnumbers 61273020and61303168.
摘要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.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence
关键词[WOS]VARIABLE SELECTION ; ORACLE PROPERTIES ; REGULARIZATION ; ALGORITHMS ; MATRICES ; LASSO ; MODEL
WOS研究方向Computer Science
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/15660
专题机器人学研究室
通讯作者Wang, Yao
作者单位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
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GB/T 7714
Cao, Wenfei,Wang, Yao,Yang, Can,et al. Folded-concave penalization approaches to tensor completion[J]. Neurocomputing,2015,152:261-273.
APA Cao, Wenfei,Wang, Yao,Yang, Can,Chang, Xiangyu,Han Z,&Xu, Zongben.(2015).Folded-concave penalization approaches to tensor completion.Neurocomputing,152,261-273.
MLA Cao, Wenfei,et al."Folded-concave penalization approaches to tensor completion".Neurocomputing 152(2015):261-273.
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