Results 241 to 250 of about 96,100 (273)

Internet traffic tensor completion with tensor nuclear norm

Computational Optimization and Applications, 2023
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Can Li, Yannan Chen, Dong-Hui Li
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Smooth Tensor Product for Tensor Completion

IEEE Transactions on Image Processing
Low-rank tensor completion (LRTC) has shown promise in processing incomplete visual data, yet it often overlooks the inherent local smooth structures in images and videos. Recent advances in LRTC, integrating total variation regularization to capitalize on the local smoothness, have yielded notable improvements.
Tongle Wu, Jicong Fan
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Weighted tensor nuclear norm minimization for tensor completion using tensor-SVD

Pattern Recognition Letters, 2020
Abstract In this paper, we consider the tensor completion problem, which aims to estimate missing values from limited information. Our model is based on the recently proposed tensor-SVD, which uses the relationships among the color channels in an image or video recovery problem. To improve the availability of the model, we propose the weighted tensor
Yang Mu, Ping Wang, Liangfu Lu, Xuyun Zhang, Lianyong Qi   +4 more
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Tensor Completion From One-Bit Observations

IEEE Transactions on Image Processing, 2019
The tensor completion issues have obtained a great deal of attention in the past few years. However, the data fidelity part minimizes a squared loss function, which may be inappropriate for the case of noisy one-bit observations. In this paper, we alleviate the mentioned difficulty by drawing on the experience of matrix scenarios.
Baohua Li, Xiaoning Zhang, Xiaoli Li, Huchuan Lu   +3 more
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Automorphisms of Tensor Completions of Algebras

Algebra and Logic, 2005
Summary: In the classical representation of different groups, frequent use is made of a linear automorphism group of various algebras. Since the linear automorphism group is only part of a full automorphism group, such an approach might seem to be too restrictive.
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Attention-Guided Low-Rank Tensor Completion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Low-rank tensor completion (LRTC) aims to recover missing data of high-dimensional structures from a limited set of observed entries. Despite recent significant successes, the original structures of data tensors are still not effectively preserved in LRTC algorithms, yielding less accurate restoration results. Moreover, LRTC algorithms often incur high
Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee   +2 more
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Completed Tensor Product and Flatness

Algebra Colloquium, 2020
We give, in a more general case than the Noetherian case, an answer to the question posed by Shaul on the flatness of the completed tensor product.
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Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

IEEE Transactions on Biomedical Engineering
Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages
Yixin Gou, Yipeng Liu, Fei He, Borbála Hunyadi, Ce Zhu   +4 more
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Tensor rank is NP-complete

Journal of Algorithms, 1989
We prove that computing the rank of a three-dimensional tensor over any finite field is NP-complete. Over the rational numbers the problem is NP-hard.
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Parallel convolutional processing using an integrated photonic tensor core

Nature, 2021
Johannes Feldmann, Nathan Youngblood
exaly