Results 41 to 50 of about 96,100 (273)
Multi-Feature Tensor Neighborhood Preserving Embedding for 3D Facial Expression Recognition
IEEE Access, 2021 To investigate an effective representation model for 3D facial expression recognition, this paper proposes a multi-feature tensor neighborhood preserving embedding (MFTNPE) method, which seeks various attribute features from raw textured shape scan ...
Yajie Jiang, Qiuqi Ruan
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Tensor Methods for Nonlinear Matrix Completion
SIAM Journal on Mathematics of Data Science, 2021 In the low-rank matrix completion (LRMC) problem, the low-rank assumption means that the columns (or rows) of the matrix to be completed are points on a low-dimensional linear algebraic variety. This paper extends this thinking to cases where the columns are points on a low-dimensional nonlinear algebraic variety, a problem we call Low Algebraic ...
Greg Ongie +4 more
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Rail transit OD‐matrix completion via manifold regularized tensor factorisation
IET Intelligent Transport Systems, 2021 Urban rail transit has become an indispensable mode in major cities worldwide regarding the advantages of large capacity, high speed, punctuality, and environmental protection.
Hanxuan Dong +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe task of hyperspectral image completion generally involves random missing entries completion, stripes inpainting, and cloud removal, which can enhance the accuracy of subsequent image analysis.
Yao Li, Yujie Zhang, Hongwei Li
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Efficient tensor completion for color image and video recovery: Low-rank tensor train
, 2016 This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its ...
Bengua, Johann A. +3 more
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Accelerated Low-Rank Tensor Completion via Projected Tensor Block Coordinate Descent
IEEE AccessThe low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis.
Geunseop Lee
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A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
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Efficient Low Rank Tensor Ring Completion
, 2017 Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS ...
Aeron, Shuchin +2 more
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Exact Tensor Completion Using t-SVD [PDF]
IEEE Transactions on Signal Processing, 2017 16 pages, 5 figures, 2 ...
Zhang, Zemin, Aeron, Shuchin
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Tensor Completion via Gaussian Process--Based Initialization [PDF]
SIAM Journal on Scientific Computing, 2020 In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Process Regression and TT-cross ...
Yermek Kapushev +2 more
openaire +3 more sources