Results 11 to 20 of about 96,100 (273)
Tensor completion in hierarchical tensor representations [PDF]
, 2014 Compressed sensing extends from the recovery of sparse vectors from undersampled measurements via efficient algorithms to the recovery of matrices of low rank from incomplete information.
A. Arnold +52 more
core +3 more sources
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 ...
Huyan Huang +3 more
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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
openaire +2 more sources
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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Dehomogenization for completely positive tensors
Numerical Algebra, Control and Optimization, 2023 25 ...
Nie, Jiawang +3 more
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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,
Montanari, Andrea, Sun, Nike
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Image Completion in Embedded Space Using Multistage Tensor Ring Decomposition
Frontiers in Artificial Intelligence, 2021 Tensor Completion is an important problem in big data processing. Usually, data acquired from different aspects of a multimodal phenomenon or different sensors are incomplete due to different reasons such as noise, low sampling rate or human mistake.
Farnaz Sedighin +3 more
doaj +1 more source
Convex Coupled Matrix and Tensor Completion [PDF]
Neural Computation, 2018 We propose a set of convex low-rank inducing norms for coupled matrices and tensors (hereafter referred to as coupled tensors), in which information is shared between the matrices and tensors through common modes. More specifically, we first propose a mixture of the overlapped trace norm and the latent norms with the matrix trace norm, and then ...
Wimalawarne, Kishan +3 more
openaire +5 more sources
Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion
Journal of Scientific Computing, 2022 Nonlinear transform, tensor nuclear norm, proximal alternating minimization, tensor ...
Ben-Zheng Li +4 more
openaire +2 more sources
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
doaj +1 more source