Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
391931 | Information Sciences | 2016 | 15 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Teng-Yu Ji, Ting-Zhu Huang, Xi-Le Zhao, Tian-Hui Ma, Gang Liu,