A Corrected Tensor Nuclear Norm Minimization Method for Noisy Low-Rank Tensor Completion
SIAM Journal on Imaging Sciences, 2019Summary: In this paper, we study the problem of low-rank tensor recovery from limited sampling with noisy observations for third-order tensors. A tensor nuclear norm method based on a convex relaxation of the tubal rank of a tensor has been used and studied for tensor completion.
Xiongjun Zhang, Michael K. Ng
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Balanced Unfolding Induced Tensor Nuclear Norms for High-Order Tensor Completion
IEEE Transactions on Neural Networks and Learning SystemsThe recently proposed tensor tubal rank has been witnessed to obtain extraordinary success in real-world tensor data completion. However, existing works usually fix the transform orientation along the third mode and may fail to turn multidimensional low-tubal-rank structure into account.
Yuning Qiu +4 more
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Sparse and Truncated Nuclear Norm Based Tensor Completion
Neural Processing Letters, 2016One of the main difficulties in tensor completion is the calculation of the tensor rank. Recently a tensor nuclear norm, which is equal to the weighted sum of matrix nuclear norms of all unfoldings of the tensor, was proposed to address this issue. However, in this approach, all the singular values are minimized simultaneously.
Zi-Fa Han +3 more
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Quaternion Tensor Completion Via Qr Decomposition and Nuclear Norm Minimization
Numerical Linear Algebra with ApplicationsABSTRACTThe task of tensor (matrix) completion has been widely used in the fields of computer vision and image processing, etc. To achieve the completion, the existing methods are mostly based on singular value decomposition of the real tensors and nuclear norm minimization.
Jian Sun, Xin Liu, Yang Zhang
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Coupled Transformed Induced Tensor Nuclear Norm for Robust Tensor Completion
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023Mengjie Qin +5 more
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Parallel convolutional processing using an integrated photonic tensor core
Nature, 2021Johannes Feldmann, Nathan Youngblood
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Tensor lattice field theory for renormalization and quantum computing
Reviews of Modern Physics, 2022Yannick Meurice +2 more
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Consensus similarity learning based on tensor nuclear norm
Machine Vision and Applications, 2022Rong Tang, Gui-Fu Lu
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Third-order nonlinear Hall effect induced by the Berry-connection polarizability tensor
Nature Nanotechnology, 2021Shen Lai, Huiying Liu, Zhaowei Zhang
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Measurement of the quantum geometric tensor and of the anomalous Hall drift
Nature, 2020Antonio Gianfrate +2 more
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