Low tensor-ring rank completion by parallel matrix factorization
Tensor-ring (TR) decomposition has recently attracted considerable attention in solving the
low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding …
low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding …
Scalable tucker factorization for sparse tensors-algorithms and discoveries
Given sparse multi-dimensional data (eg,(user, movie, time; rating) for movie
recommendations), how can we discover latent concepts/relations and predict missing …
recommendations), how can we discover latent concepts/relations and predict missing …
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach Using ℓp-Regression for Outlier Resistance
Low-rank tensor completion (LRTC) that aims to restore the latent clean data from an
incomplete and/or degraded observation, shows promising results in ubiquitous tensorial …
incomplete and/or degraded observation, shows promising results in ubiquitous tensorial …
Robust to rank selection: Low-rank sparse tensor-ring completion
J Yu, G Zhou, W Sun, S Xie - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
Tensor-ring (TR) decomposition was recently studied and applied for low-rank tensor
completion due to its powerful representation ability of high-order tensors. However, most of …
completion due to its powerful representation ability of high-order tensors. However, most of …
Online subspace learning and imputation by tensor-ring decomposition
J Yu, T Zou, G Zhou - Neural Networks, 2022 - Elsevier
This paper considers the completion problem of a partially observed high-order streaming
data, which is cast as an online low-rank tensor completion problem. Though the online low …
data, which is cast as an online low-rank tensor completion problem. Though the online low …
Robust Low-Rank Tensor Completion Based on Tensor Ring Rank via -Norm
Tensor completion aims to recover missing entries given incomplete multi-dimensional data
by making use of the prior low-rank information, and has various applications because many …
by making use of the prior low-rank information, and has various applications because many …
Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful
capability of recovering missing entries. However, it has to repeatedly calculate the time …
capability of recovering missing entries. However, it has to repeatedly calculate the time …
FERLrTc: 2D+ 3D facial expression recognition via low-rank tensor completion
In this paper, a 4D tensor model is firstly constructed to explore efficient structural
information and correlations from multi-modal data (both 2D and 3D face data). As the …
information and correlations from multi-modal data (both 2D and 3D face data). As the …
Tensor decomposition based beamspace ESPRIT for millimeter wave MIMO channel estimation
We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO
channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method …
channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method …
A nonlocal self-similarity-based weighted tensor low-rank decomposition for multichannel image completion with mixture noise
M Xie, X Liu, X Yang - IEEE Transactions on Neural Networks …, 2022 - ieeexplore.ieee.org
Multichannel image completion with mixture noise is a challenging problem in the fields of
machine learning, computer vision, image processing, and data mining. Traditional image …
machine learning, computer vision, image processing, and data mining. Traditional image …