Tensor completion using total variation and low-rank matrix factorization

被引：139
|
作者：
Ji, Teng-Yu [1 ]
Huang, Ting-Zhu [1 ]
Zhao, Xi-Le [1 ]
Ma, Tian-Hui [1 ]
Liu, Gang [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Resrarch Ctr Image & Vis Comp, Chengdu 611731, Sichuan, Peoples R China
来源：
INFORMATION SCIENCES | 2016年 / 326卷
关键词：
Tensor completion; Total variation; Low-rank matrix factorization; Block coordinate descent; ALGORITHM;
D O I：
10.1016/j.ins.2015.07.049
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm converges to the coordinatewise minimizers. Experimental results are reported to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码：243 / 257
页数：15
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