Results 21 to 30 of about 1,781 (261)
Cross: Efficient low-rank tensor completion [PDF]
The Annals of Statistics, 2019 The completion of tensors, or high-order arrays, attracts significant attention in recent research. Current literature on tensor completion primarily focuses on recovery from a set of uniformly randomly measured entries, and the required number of measurements to achieve recovery is not guaranteed to be optimal.
openaire +4 more sources
Tensor Factorization for Low-Rank Tensor Completion
IEEE Transactions on Image Processing, 2018 Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its ...
Pan Zhou +3 more
openaire +3 more sources
Low rank tensor completion for multiway visual data [PDF]
Signal Processing, 2019 Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data acquisition and transformation. In dis paper, we provide an overview of recent development in low rank tensor completion for estimating teh missing components of visual data, e. g. , color images and videos.
Zhen Long +3 more
openaire +2 more sources
Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
IEEE Access, 2019 In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor ranks.
Yaru Su, Xiaohui Wu, Wenxi Liu
doaj +1 more source
Low-Rank Tensor Completion via Tensor Nuclear Norm With Hybrid Smooth Regularization
IEEE Access, 2019 As a convex surrogate of tensor multi rank, recently the tensor nuclear norm (TNN) obtains promising results in the tensor completion. However, only considering the low-tubal-rank prior is not enough for recovering the target tensor, especially when the ...
Xi-Le Zhao +4 more
doaj +1 more source
Nonconvex Tensor Relative Total Variation for Image Completion
Mathematics, 2023 Image completion, which falls to a special type of inverse problems, is an important but challenging task. The difficulties lie in that (i) the datasets usually appear to be multi-dimensional; (ii) the unavailable or corrupted data entries are randomly ...
Yunqing Bai, Jihong Pei, Min Li
doaj +1 more source
Low-rank tensor completion by Riemannian optimization [PDF]
BIT Numerical Mathematics, 2013 The authors propose a variant of the nonlinear conjugate gradient method for solving the tensor completion problem that employs the framework Riemannian optimization techniques on the manifold of tensors of fixed multilinear rank. Some preliminary numerical results are reported to investigate the effectiveness of the proposed algorithm.
Kressner Daniel +2 more
openaire +2 more sources
Incomplete Multiview Clustering via Low‐Rank Tensor Ring Completion
International Journal of Intelligent Systems, 2023 Since real‐world multiview data frequently contains numerous samples that are not observed from some viewpoints, the incomplete multiview clustering (IMC) issue has received a great deal of attention recently. However, most existing IMC methods choose to zero‐fill the missing instances, which leads to the failure to exploit information hidden in the ...
Jinshi Yu +4 more
openaire +1 more source
Dictionary Learning With Low-Rank Coding Coefficients for Tensor Completion [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 In this paper, we propose a novel tensor learning and coding model for third-order data completion. Our model is to learn a data-adaptive dictionary from the given observations, and determine the coding coefficients of third-order tensor tubes.
Tai-Xiang Jiang +3 more
openaire +3 more sources
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