Results 21 to 30 of about 96,100 (273)
Missing Data Recovery Based on Tensor-CUR Decomposition
IEEE Access, 2018 Tensor completion is a higher way analog of matrix completion, which has proven to be a powerful tool for data analysis. In this paper, we formulate the missing data recovery problem of a three-way tensor as a tensor completion problem.
Lele Wang +3 more
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Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
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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Orthogonal random projection for tensor completion
IET Computer Vision, 2020 The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD ...
Yali Feng, Guoxu Zhou
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Contour information regularized tensor ring completion for realistic image restoration
IET Image Processing, 2022 Tensor completion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low‐rank nature of images, implicitly requiring that the whole picture is of ...
Zhi Yu, Yihao Luo, Zhifa Liu, Guoxu Zhou
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Frontiers in Artificial Intelligence, 2022 A major challenge in many machine learning tasks is that the model expressive power depends on model size. Low-rank tensor methods are an efficient tool for handling the curse of dimensionality in many large-scale machine learning models.
Kaiqi Zhang, Cole Hawkins, Zheng Zhang
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Tensor Factorization for Low-Rank Tensor Completion
IEEE Transactions on Image Processing, 2018 Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its ...
Pan Zhou +3 more
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Taking the 4D Nature of fMRI Data Into Account Promises Significant Gains in Data Completion
IEEE Access, 2021 Functional magnetic resonance imaging (fMRI) is a powerful, noninvasive tool that has significantly contributed to the understanding of the human brain.
Irina Belyaeva +3 more
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Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion
IEEE Transactions on Neural Networks and Learning Systems, 2022 Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose a new multilayer sparsity-based tensor decomposition (MLSTD) for the low-rank tensor completion (LRTC).
Jize Xue +5 more
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A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction
IEEE Access, 2016 Spectrum data, which are usually characterized by many dimensions, such as location, frequency, time, and signal strength, present formidable challenges in terms of acquisition, processing, and visualization.
Mengyun Tang +4 more
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Tensor Completion via Smooth Rank Function Low-Rank Approximate Regularization
Remote Sensing, 2023 In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data.
Shicheng Yu +5 more
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