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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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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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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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Provable tensor ring completion [PDF]
Signal Processing, 2020 Tensor completion recovers a multi-dimensional array from a limited number of measurements. Using the recently proposed tensor ring (TR) decomposition, in this paper we show that a d-order tensor of dimensional size n and TR rank r can be exactly recovered with high probability by solving a convex optimization program, given n^{d/2} r^2 ln^7(n^{d/2 ...
Ce Zhu, Huyan Huang, Yipeng Liu
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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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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, Canyi Lu, Zhouchen Lin
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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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Apply Optimized Tensor Completion Method by Bayesian CP-Factorization for Image Recovery [PDF]
Control and Optimization in Applied Mathematics, 2021 In this paper, we are going to analyze big data (embedded in the digital images) with new methods of tensor completion (TC). The determination of tensor ranks and the type of decomposition are significant and essential matters.
Ali Reza Shojaeifard +2 more
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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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