Results 21 to 30 of about 9,676 (259)
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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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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Dehomogenization for completely positive tensors
Numerical Algebra, Control and Optimization, 2023 25 ...
Nie, Jiawang +3 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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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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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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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
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Tensor Completion Made Practical
CoRR, 2020 NeurIPS ...
Allen Liu, Ankur Moitra
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A Biased Deep Tensor Factorization Network For Tensor Completion
CoRR, 2021 Tensor decomposition is a popular technique for tensor completion, However most of the existing methods are based on linear or shallow model, when the data tensor becomes large and the observation data is very small, it is prone to over fitting and the performance decreases significantly.
Qianxi Wu, An-Bao Xu
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Missing Data Recovery Based on Tensor-CUR Decomposition
IEEE Access, 2018 Tensor completion is a higher way analog of matrix completion, which has proven to be a powerful tool for data analysis. In this paper, we formulate the missing data recovery problem of a three-way tensor as a tensor completion problem.
Lele Wang +3 more
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