Tensor completion using enhanced multiple modes low-rank prior and total variation
This work addresses tensor completion for applications like data imputation and signal processing, representing an incremental improvement over prior methods.
The paper tackles the problem of recovering low-rank tensors from incomplete data by proposing a novel model that uses double nuclear norm regularization and matrix factorization across all modes, achieving recovery from significantly fewer samples than existing methods.
In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An block successive upper-bound minimization algorithm is applied to solve the model. Subsequence convergence of our algorithm can be established, and our algorithm converges to the coordinate-wise minimizers in some mild conditions. Several experiments on three types of public data sets show that our algorithm can recover a variety of low-rank tensors from significantly fewer samples than the other testing tensor completion methods.