Results 11 to 20 of about 96,220 (224)
A New Model for Tensor Completion: Smooth Convolutional Tensor Factorization
IEEE Access, 2023 Tensor completion is the problem of filling-in missing parts of multidimensional data using the values of the reference elements. Recently, Multiway Delay-embedding Transform (MDT), which considers a low-dimensional space in a delay-embedded space with ...
Hiromu Takayama, Tatsuya Yokota
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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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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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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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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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Tensor Completion Methods for Collaborative Intelligence
IEEE Access, 2020 In the race to bring Artificial Intelligence (AI) to the edge, collaborative intelligence has emerged as a promising way to lighten the computation load on edge devices that run applications based on Deep Neural Networks (DNNs).
Lior Bragilevsky, Ivan V. Bajic
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