Folded-concave penalization approaches to tensor completion
文献类型：期刊论文
| 作者 | Cao, Wenfei; Wang, Yao; Yang, Can; Chang, Xiangyu; Han Z(韩志) ; Xu, Zongben
|
| 刊名 | Neurocomputing
 |
| 出版日期 | 2015 |
| 卷号 | 152页码:261-273 |
| 关键词 | Tensor completion Nuclear norm Folded-concave penalization Local linear approximation Sparse learning |
| ISSN号 | 0925-2312 |
| 产权排序 | 3 |
| 通讯作者 | Wang, Yao |
| 中文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | VARIABLE SELECTION ; ORACLE PROPERTIES ; REGULARIZATION ; ALGORITHMS ; MATRICES ; LASSO ; MODEL |
| 收录类别 | SCI ; EI |
| 资助信息 | National 973 Project of China under Grant number 2013 CB329404 and Natural Science Foundation of China under Grantnumbers 61273020and61303168. |
| 语种 | 英语 |
| WOS记录号 | WOS:000349572600027 |
| 公开日期 | 2015-02-04 |
| 源URL | [http://ir.sia.cn/handle/173321/15660]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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