Results 31 to 40 of about 10,455 (264)
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 via Smooth Rank Function Low-Rank Approximate Regularization
Remote Sensing, 2023 In recent years, the tensor completion algorithm has played a vital part in the reconstruction of missing elements within high-dimensional remote sensing image data.
Shicheng Yu +5 more
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Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods
IEEE Access, 2020 Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data ...
Qin Li +4 more
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Accelerated non-negative tensor completion via integer programming
Frontiers in Applied Mathematics and Statistics, 2023 The problem of tensor completion has applications in healthcare, computer vision, and other domains. However, past approaches to tensor completion have faced a tension in that they either have polynomial-time computation but require exponentially more ...
Wenhao Pan +3 more
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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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Tensor Completion via Tensor Networks with a Tucker Wrapper
CoRR, 2020 In recent years, low-rank tensor completion (LRTC) has received considerable attention due to its applications in image/video inpainting, hyperspectral data recovery, etc. With different notions of tensor rank (e.g., CP, Tucker, tensor train/ring, etc.), various optimization based numerical methods are proposed to LRTC.
Yunfeng Cai, Ping Li 0001
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The Geometry of Rank-One Tensor Completion [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 The geometry of the set of restrictions of rank-one tensors to some of their coordinates is studied. This gives insight into the problem of rank-one completion of partial tensors. Particular emphasis is put on the semialgebraic nature of the problem, which arises for real tensors with constraints on the parameters.
Kahle, Thomas +4 more
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Covariate-Assisted Sparse Tensor Completion
Journal of the American Statistical Association, 2022 To Appear in Journal of the American Statistical ...
Hilda S. Ibriga, Will Wei Sun
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Rank revealing‐based tensor completion using improved generalized tensor multi‐rank minimization
IET Signal Processing, 2021 The authors address the problem of tensor completion from limited samplings. An improved generalized tubal Kronecker decomposition is first proposed to reveal the tensor structure of the targeted data, and the improved generalized tensor tubal‐rank and ...
Wei Z. Sun, Peng Zhang, Bo Zhao
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Riemannian preconditioning for tensor completion
CoRR, 2015 We propose a novel Riemannian preconditioning approach for the tensor completion problem with rank constraint. A Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry in Tucker decomposition.
Hiroyuki Kasai, Bamdev Mishra
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