Results 11 to 20 of about 9,676 (259)
Rank-Adaptive Tensor Completion Based on Tucker Decomposition [PDF]
Entropy, 2023 Tensor completion is a fundamental tool to estimate unknown information from observed data, which is widely used in many areas, including image and video recovery, traffic data completion and the multi-input multi-output problems in information theory ...
Siqi Liu, Xiaoyu Shi, Qifeng Liao
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HOSVD-Based Algorithm for Weighted Tensor Completion [PDF]
Journal of Imaging, 2021 Matrix completion, the problem of completing missing entries in a data matrix with low-dimensional structure (such as rank), has seen many fruitful approaches and analyses.
Zehan Chao +2 more
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Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion [PDF]
Sensors, 2023 Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance.
Xiaohua Liu, Guijin Tang
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Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion. [PDF]
PLoS Computational Biology, 2021 High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample.
Zhuliu Li +3 more
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Orthogonal random projection for tensor completion [PDF]
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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Efficient enhancement of low-rank tensor completion via thin QR decomposition [PDF]
Frontiers in Big DataLow-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues.
Yan Wu, Yunzhi Jin
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Tensor Completion Method Based on Coupled Random Projection [PDF]
Jisuanji kexue, 2021 In modern signal processing,the date with large scale,high dimension and complex structure need to be stored and analyzed in more and more fields.Tensors,as a high-order extension of vectors and matrices,can more intuitively represent the structure of ...
YANG Hong-xin, SONG Bao-yan, LIU Ting-ting, DU Yue-feng, LI Xiao-guang
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Completely Positive Binary Tensors [PDF]
Mathematics of Operations Research, 2019 A symmetric tensor is completely positive (CP) if it is a sum of tensor powers of nonnegative vectors. This paper characterizes completely positive binary tensors. We show that a binary tensor is completely positive if and only if it satisfies two linear matrix inequalities.
Jinyan Fan, Jiawang Nie, Anwa Zhou
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Spectral Algorithms for Tensor Completion [PDF]
Communications on Pure and Applied Mathematics, 2018 In the tensor completion problem, one seeks to estimate a low‐rank tensor based on a random sample of revealed entries. In terms of the required sample size, earlier work revealed a large gap between estimation with unbounded computational resources (using, for instance, tensor nuclear norm minimization) and polynomial‐time algorithms. Among the latter,
Andrea Montanari, Nike Sun
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A parallel multi‐block alternating direction method of multipliers for tensor completion
IET Image Processing, 2021 This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global ...
Hu Zhu +5 more
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