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An efficient tensor completion method via truncated nuclear norm

Song, Yun and Li, Jie and Chen, Xi and Zhang, Dengyong and Tang, Qiang and Yang, Kun (2020) 'An efficient tensor completion method via truncated nuclear norm.' Journal of Visual Communication and Image Representation, 70. p. 102791. ISSN 1047-3203 (In Press)

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Tensor completion aims to recover missing entries from partial observations for multi-dimensional data. Traditional tensor completion algorithms process the dimensional data by unfolding the tensor into matrices, which breaks the inherent correlation and dependencies in multiple channels and lead to critical information loss. In this paper, we propose a novel tensor completion model for visual multi-dimensional data completion under the tensor singular value decomposition (t-SVD) framework. In the proposed method, tensor is treated as a whole and a truncated nuclear norm regularization is employed to exploit the structural properties in a tensor and hidden information existing among the adjacent channels of a tensor. Besides, we introduce a weighted tensor to adjust the residual error of each frontal slices in consideration of their different recovery statistics. It does enhance the sparsity of all unfoldings of the tensor and accelerates the convergence of the proposed method. Experimental results on various visual datasets demonstrate the promising performance of the proposed method in comparison with the state-of-the-art tensor completion methods.

Item Type: Article
Uncontrolled Keywords: Tensor completion; Tensor singular value decomposition; Truncated tensor nuclear norm; Visual data restoration
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Jun 2020 09:04
Last Modified: 15 Jan 2022 01:33

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