Results 221 to 230 of about 1,781 (261)

Low Rank Tensor Completion with Poisson Observations

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Poisson observations for videos are important models in video processing and computer vision. In this paper, we study the third-order tensor completion problem with Poisson observations. The main aim is to recover a tensor based on a small number of its Poisson observation entries.
Xiongjun, Zhang, Michael K, Ng
openaire   +2 more sources

Nonlocal Low-Rank Tensor Completion for Visual Data

IEEE Transactions on Cybernetics, 2021
In this paper, we propose a novel nonlocal patch tensor-based visual data completion algorithm and analyze its potential problems. Our algorithm consists of two steps: the first step is initializing the image with triangulation-based linear interpolation and the second step is grouping similar nonlocal patches as a tensor then applying the proposed ...
Lefei Zhang, Liangchen Song, Bo Du, Yipeng Zhang   +3 more
openaire   +2 more sources

Tensor Completion via Nonlocal Low-Rank Regularization

IEEE Transactions on Cybernetics, 2019
Tensor completion (TC), aiming to recover original high-order data from its degraded observations, has recently drawn much attention in hyperspectral images (HSIs) domain. Generally, the widely used TC methods formulate the rank minimization problem with a convex trace norm penalty, which shrinks all singular values equally, and may generate a much ...
Ting Xie, Shutao Li, Leyuan Fang, Licheng Liu   +3 more
openaire   +2 more sources

Imbalanced low-rank tensor completion via latent matrix factorization

Neural Networks, 2022
Tensor completion has been widely used in computer vision and machine learning. Most existing tensor completion methods empirically assume the intrinsic tensor is simultaneous low-rank in all over modes. However, tensor data recorded from real-world applications may conflict with these assumptions, e.g., face images taken from different subjects often ...
Qiu, Yuning, Zhou, Guoxu, Zeng, Junhua, Zhao, Qibin, Xie, Shengli   +4 more
openaire   +2 more sources

Auto-weighted robust low-rank tensor completion via tensor-train

Information Sciences, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Chuan, Wu, Zhe-Bin, Chen, Zi-Tai, Zheng, Zi-Bin, Zhang, Xiong-Jun   +4 more
openaire   +2 more sources

Iterative tensor eigen rank minimization for low-rank tensor completion

Information Sciences, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Su, Liyu, Liu, Jing, Tian, Xiaoqing, Huang, Kaiyu, Tan, Shuncheng   +4 more
openaire   +2 more sources

Enhanced Sparsity Prior Model for Low-Rank Tensor Completion

IEEE Transactions on Neural Networks and Learning Systems, 2020
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data lies in the global subspace. The so-called global sparsity prior is measured by the tensor nuclear norm. Such assumption is not reliable in recovering low-rank (LR) tensor data, especially when considerable elements of data are missing. To mitigate this
Jize Xue, Yongqiang Zhao, Wenzhi Liao, Jonathan Cheung-Wai Chan, Seong G. Kong   +4 more
openaire   +2 more sources

Attention-Guided Low-Rank Tensor Completion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Low-rank tensor completion (LRTC) aims to recover missing data of high-dimensional structures from a limited set of observed entries. Despite recent significant successes, the original structures of data tensors are still not effectively preserved in LRTC algorithms, yielding less accurate restoration results. Moreover, LRTC algorithms often incur high
Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee   +2 more
openaire   +2 more sources

A Weighted Tensor Factorization Method for Low-Rank Tensor Completion

2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), 2019
Recently, low-rank tensor completion has attracted increasing attention in recovering incomplete tensor whose elements are missing. The basic assumption is that the underlying tensor is a low-rank tensor, and therefore tensor nuclear norm minimization can be applied to recover such tensor.
Miaomiao Cheng, Liping Jing, Michael K. Ng   +2 more
openaire   +1 more source

A tensor network low rank completion method

Computational and Applied Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bentbib, Abdeslem Hafid, Jbilou, Khalide, Khobizy, Sanaa   +2 more
openaire   +2 more sources