Results 11 to 20 of about 1,781 (261)

Robust Low-Rank Tensor Ring Completion [PDF]

IEEE Transactions on Computational Imaging, 2020
Low-rank tensor completion recovers missing entries based on different tensor decompositions. Due to its outstanding performance in exploiting some higher-order data structure, low rank tensor ring has been applied in tensor completion. To further deal with its sensitivity to sparse component as it does in tensor principle component analysis, we ...
Huyan Huang, Yipeng Liu, Zhen Long, Ce Zhu   +3 more
openaire   +2 more sources

General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification

Frontiers in Artificial Intelligence, 2022
A major challenge in many machine learning tasks is that the model expressive power depends on model size. Low-rank tensor methods are an efficient tool for handling the curse of dimensionality in many large-scale machine learning models.
Kaiqi Zhang, Cole Hawkins, Zheng Zhang
doaj   +1 more source

Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion

IEEE Transactions on Neural Networks and Learning Systems, 2022
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose a new multilayer sparsity-based tensor decomposition (MLSTD) for the low-rank tensor completion (LRTC).
Jize Xue, Yongqiang Zhao, Shaoguang Huang, Wenzhi Liao, Jonathan Cheung-Wai Chan, Seong G. Kong   +5 more
openaire   +4 more sources

Thick Cloud Removal in Multi-Temporal Remote Sensing Images via Frequency Spectrum-Modulated Tensor Completion

Remote Sensing, 2023
Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images ...
Zhihong Chen, Peng Zhang, Yu Zhang, Xunpeng Xu, Luyan Ji, Hairong Tang   +5 more
doaj   +1 more source

Effective Incomplete Multi-View Clustering via Low-Rank Graph Tensor Completion

Mathematics, 2023
In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view ...
Jinshi Yu, Qi Duan, Haonan Huang, Shude He, Tao Zou   +4 more
doaj   +1 more source

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, Yuqing Shi, Wenjin Hu, Weilan Wang, Jing Lian   +4 more
doaj   +1 more source

Accurate Tensor Completion via Adaptive Low-Rank Representation [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2020
Low-rank representation-based approaches that assume low-rank tensors and exploit their low-rank structure with appropriate prior models have underpinned much of the recent progress in tensor completion. However, real tensor data only approximately comply with the low-rank requirement in most cases, viz., the tensor consists of low-rank (e.g ...
Lei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton van den Hengel, Yanning Zhang   +5 more
openaire   +3 more sources

Contour information regularized tensor ring completion for realistic image restoration

IET Image Processing, 2022
Tensor completion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low‐rank nature of images, implicitly requiring that the whole picture is of ...
Zhi Yu, Yihao Luo, Zhifa Liu, Guoxu Zhou
doaj   +1 more source

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, Fei Cheng, Hang Zheng, Zhiguo Shi, Chengwei Zhou   +4 more
doaj   +1 more source

Efficient Low Rank Tensor Ring Completion [PDF]

2017 IEEE International Conference on Computer Vision (ICCV), 2017
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation.
Wang, Wenqi, Aggarwal, Vaneet, Aeron, Shuchin   +2 more
openaire   +2 more sources