Tensor Completion Based on Triple Tubal Nuclear Norm [PDF]
Algorithms, 2018 Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos ...
Dongxu Wei +4 more
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Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization [PDF]
IEEE Access, 2019 In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor ranks.
Yaru Su, Xiaohui Wu, Wenxi Liu
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An Efficient Tensor Completion Method Via New Latent Nuclear Norm [PDF]
IEEE Access, 2020 In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding schemes.
Jinshi Yu +3 more
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Interpretable nonconvex submodule clustering algorithm using ℓr-induced tensor nuclear norm and ℓ2,p column sparse norm with global convergence guarantees. [PDF]
PLoS ONETensor-based subspace clustering algorithms have garnered significant attention for their high efficiency in clustering high-dimensional data. However, when dealing with 2D image data, traditional vectorization operations in most algorithms tend to ...
Ming Yang +3 more
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Symmetric Tensor Nuclear Norms [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 25 ...
Jiawang Nie
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A concise proof to the spectral and nuclear norm bounds through tensor partitions [PDF]
Open Mathematics, 2019 On estimations of the lower and upper bounds for the spectral and nuclear norm of a tensor, Li established neat bounds for the two norms based on regular tensor partitions, and proposed a conjecture for the same bounds to be hold based on general tensor ...
Kong Xu
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Reshaped tensor nuclear norms for higher order tensor completion [PDF]
Machine Learning, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kishan Wimalawarne, Hiroshi Mamitsuka
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Multiview Subspace Clustering by an Enhanced Tensor Nuclear Norm [PDF]
IEEE Transactions on Cybernetics, 2021 Despite the promising preliminary results, tensor-singular value decomposition (t-SVD)-based multiview subspace is incapable of dealing with real problems, such as noise and illumination changes. The major reason is that tensor-nuclear norm minimization (TNNM) used in t-SVD regularizes each singular value equally, which does not make sense in matrix ...
Wei Xia +5 more
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Spectral norm and nuclear norm of a third order tensor
Journal of Industrial & Management Optimization, 2021 <p style="text-indent:20px;">The spectral norm and the nuclear norm of a third order tensor play an important role in the tensor completion and recovery problem. We show that the spectral norm of a third order tensor is equal to the square root of the spectral norm of three positive semi-definite biquadratic tensors, and the square roots of the ...
Liqun Qi, Shenglong Hu, Yanwei Xu
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Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion [PDF]
Journal of Scientific Computing, 2021 Nonlinear transform, tensor nuclear norm, proximal alternating minimization, tensor ...
Ben-Zheng Li +4 more
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