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Robust Tensor Factorization for Color Image and Grayscale Video Recovery
IEEE Access, 2020 Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory.
Shiqiang Du +4 more
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Latent Matrices for Tensor Network Decomposition and to Tensor Completion
CoRR, 2022 The prevalent fully-connected tensor network (FCTN) has achieved excellent success to compress data. However, the FCTN decomposition suffers from slow computational speed when facing higher-order and large-scale data. Naturally, there arises an interesting question: can a new model be proposed that decomposes the tensor into smaller ones and speeds up ...
Peilin Yang +3 more
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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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A Biased Deep Tensor Factorization Network For Tensor Completion
CoRR, 2021 Tensor decomposition is a popular technique for tensor completion, However most of the existing methods are based on linear or shallow model, when the data tensor becomes large and the observation data is very small, it is prone to over fitting and the performance decreases significantly.
Qianxi Wu, An-Bao Xu
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Hierarchical Tensor Ring Completion
CoRR, 2020 Tensor completion can estimate missing values of a high-order data from its partially observed entries. Recent works show that low rank tensor ring approximation is one of the most powerful tools to solve tensor completion problem. However, existing algorithms need predefined tensor ring rank which may be hard to determine in practice.
Abdul Ahad +3 more
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Convex Coupled Matrix and Tensor Completion [PDF]
Neural Computation, 2018 We propose a set of convex low-rank inducing norms for coupled matrices and tensors (hereafter referred to as coupled tensors), in which information is shared between the matrices and tensors through common modes. More specifically, we first propose a mixture of the overlapped trace norm and the latent norms with the matrix trace norm, and then ...
Wimalawarne, Kishan +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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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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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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