Efficient Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation

• URL: http://arxiv.org/abs/2109.05736v1
• Date: Mon, 13 Sep 2021 06:50:37 GMT
• Title: Efficient Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation
• Authors: Yang Zhang, Yao Wang, Zhi Han, Xi'ai Chen, Yandong Tang
• Abstract summary: We propose a novel tensor completion approach via the element-wise weighted technique. We specifically consider the recovery quality of edge elements from adjacent blocks. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods.
• Score: 18.438177637687357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there have been an increasing number of applications of tensor completion based on the tensor train (TT) format because of its efficiency and effectiveness in dealing with higher-order tensor data. However, existing tensor completion methods using TT decomposition have two obvious drawbacks. One is that they only consider mode weights according to the degree of mode balance, even though some elements are recovered better in an unbalanced mode. The other is that serious blocking artifacts appear when the missing element rate is relatively large. To remedy such two issues, in this work, we propose a novel tensor completion approach via the element-wise weighted technique. Accordingly, a novel formulation for tensor completion and an efficient optimization algorithm, called as tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed. In addition, we specifically consider the recovery quality of edge elements from adjacent blocks. Different from traditional reshaping and ket augmentation, we utilize a new tensor augmentation technique called overlapping ket augmentation, which can further avoid blocking artifacts. We then conduct extensive performance evaluations on synthetic data and several real image data sets. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods.

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