SIA OpenIR  > 机器人学研究室
Folded-concave penalization approaches to tensor completion
Cao WF(曹文飞); Wang Y(王尧); Yang, Can; Chang, Xiangyu; Han Z(韩志); Xu ZB(徐宗本)
Source PublicationNeurocomputing
Indexed BySCI ; EI
EI Accession number20150400440904
WOS IDWOS:000349572600027
Contribution Rank3
Funding OrganizationNational 973 Project of China under Grant number 2013 CB329404 and Natural Science Foundation of China under Grantnumbers 61273020and61303168.
KeywordTensor Completion Nuclear Norm Folded-concave Penalization Local Linear Approximation Sparse Learning

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.

WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence
WOS KeywordVariable Selection ; Oracle Properties ; Regularization ; Algorithms ; Matrices ; Lasso ; Model
WOS Research AreaComputer Science
Citation statistics
Cited Times:25[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorWang Y(王尧)
Affiliation1.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.
Files in This Item:
File Name/Size DocType Version Access License
Folded-concave penal(11144KB)期刊论文出版稿开放获取ODC PDDLView Application Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Cao WF(曹文飞)]'s Articles
[Wang Y(王尧)]'s Articles
[Yang, Can]'s Articles
Baidu academic
Similar articles in Baidu academic
[Cao WF(曹文飞)]'s Articles
[Wang Y(王尧)]'s Articles
[Yang, Can]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cao WF(曹文飞)]'s Articles
[Wang Y(王尧)]'s Articles
[Yang, Can]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Folded-concave penalization approaches to tensor completion.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.