Tensor completion using enhanced multiple modes low-rank prior and total variation

• URL: http://arxiv.org/abs/2004.08747v3
• Date: Wed, 6 May 2020 00:57:57 GMT
• Title: Tensor completion using enhanced multiple modes low-rank prior and total variation
• Authors: Haijin Zeng, Xiaozhen Xie, Jifeng Ning
• Abstract summary: 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. Subsequence convergence of our algorithm can be established, and our algorithm converges to the coordinate-wise minimizers in some mild conditions.
• Score: 1.3406858660972554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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