Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6856651 | Information Sciences | 2018 | 31 Pages |
Abstract
In this paper, we propose a novel tensor completion model using framelet regularization and low-rank matrix factorization. An effective block successive upper-bound minimization (BSUM) algorithm is designed to solve the proposed optimization model. The convergence of our algorithm is theoretically guaranteed, and under some mild conditions, our algorithm converges to the coordinate-wise minimizers. Extensive experiments are conducted on the synthetic data and real data, and the results demonstrate the effectiveness and the efficiency of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Teng-Yu Ji, Liang-Jian Deng,