Tensor Completion Using Kronecker Rank-1 Tensor Train With Application to Visual Data Inpainting
Tensor Completion Using Kronecker Rank-1 Tensor Train With Application to Visual Data Inpainting
复制标题
使用 Kronecker Rank-1 张量训练完成张量并应用于视觉数据修复
DOI：
10.1109/access.2018.2866194
复制
发表时间：
2018
期刊：
影响因子：
3.9
通讯作者：
So Hing Cheung
中科院分区：
文献类型：
--
作者：
Sun Weize;Chen Yuan;So Hing Cheung
The problem of data reconstruction with partly sampled elements under a tensor structure, which is referred to as tensor completion, is addressed in this paper. The properties of the rank-1 tensor train decomposition and the tensor Kronecker decomposition are introduced at first, and then the tensor Kronecker rank as well as Kronecker rank-1 tensor train decomposition are defined. The general tensor completion idea is presented following the criterion of minimizing the number of Kronecker rank-1 tensors, which is relaxed to the thresholding problem and the solution is derived. Furthermore, the number of Kronecker rank-1 tensors that the proposed algorithm can retrieve and its complexity order are analyzed. Computer simulations are carried out on real visual data sets and demonstrate that our method yields a superior performance over the state-of-the-art approaches in terms of recovery accuracy and/or computational complexity.
