Results 11 to 20 of about 1,952,092 (277)
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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On the Synergy between Nonconvex Extensions of the Tensor Nuclear Norm for Tensor Recovery [PDF]
Signals, 2021 Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank—e.g., the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank.
Kaito Hosono +2 more
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Multiview Subspace Clustering by an Enhanced Tensor Nuclear Norm [PDF]
IEEE Transactions on Cybernetics, 2022 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 0007 +5 more
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A concise proof to the spectral and nuclear norm bounds through tensor partitions
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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Low-Rank Tensor Completion for Image and Video Recovery via Capped Nuclear Norm [PDF]
IEEE Access, 2019 Inspired by the robustness and efficiency of the capped nuclear norm, in this paper, we apply it to 3D tensor applications and propose a novel low-rank tensor completion method via tensor singular value decomposition (t-SVD) for image and video recovery.
Xi Chen +5 more
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Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion
Neurocomputing, 2021 Abstract Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address this drawback, we, based on the tensor-tensor product (t-product), propose a new LRTC method-the unified ...
Du S. +4 more
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Accelerated Tensor Robust Principal Component Analysis via Factorized Tensor Norm Minimization
Applied SciencesIn this paper, we aim to develop an efficient algorithm for the solving Tensor Robust Principal Component Analysis (TRPCA) problem, which focuses on obtaining a low-rank approximation of a tensor by separating sparse and impulse noise.
Geunseop Lee
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Mathematics, 2020 Reconstruction of 3D objects in various tomographic measurements is an important problem which can be naturally addressed within the mathematical framework of 3D tensors.
Mohamed Ibrahim Assoweh +2 more
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A Tensor Regularized Nuclear Norm Method for Image and Video Completion
Journal of Optimization Theory and Applications, 2021 In the present paper, we propose two new methods for tensor completion of third-order tensors. The proposed methods consist in minimizing the average rank of the underlying tensor using its approximate function, namely the tensor nuclear norm.
Abdeslem Hafid Bentbib +3 more
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Tensor Completion with BMD Factor Nuclear Norm Minimization [PDF]
CoRR10 ...
Fan Tian +4 more
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