Results 11 to 20 of about 9,784 (260)
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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Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure
Applied Sciences, 2023 Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they cannot ...
Bin Li +4 more
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Dehomogenization for completely positive tensors
Numerical Algebra, Control and Optimization, 2023 25 ...
Nie, Jiawang +3 more
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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
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Tensor Completion in Hierarchical Tensor Representations [PDF]
, 2015 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. Here we consider a further extension to the reconstruction of tensors of low multi-linear rank in recently introduced hierarchical tensor formats from a small number ...
Holger Rauhut +2 more
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A New Model for Tensor Completion: Smooth Convolutional Tensor Factorization
IEEE Access, 2023 Tensor completion is the problem of filling-in missing parts of multidimensional data using the values of the reference elements. Recently, Multiway Delay-embedding Transform (MDT), which considers a low-dimensional space in a delay-embedded space with ...
Hiromu Takayama, Tatsuya Yokota
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Deterministic Tensor Completion with Hypergraph Expanders
SIAM Journal on Mathematics of Data Science, 2021 We provide a novel analysis of low-rank tensor completion based on hypergraph expanders. As a proxy for rank, we minimize the max-quasinorm of the tensor, which generalizes the max-norm for matrices. Our analysis is deterministic and shows that the number of samples required to approximately recover an order-$t$ tensor with at most $n$ entries per ...
Kameron Decker Harris, Yizhe Zhu
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Tensor Completion Made Practical
CoRR, 2020 NeurIPS ...
Allen Liu, Ankur Moitra
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