Noisy tensor completion via low-rank tensor ring
Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to
predict missing entries from partial observations. However, existing methods often make the …
predict missing entries from partial observations. However, existing methods often make the …
Randomized sampling techniques based low-tubal-rank plus sparse tensor recovery
Recently, tensor robust principal component analysis (TRPCA) based on the tensor singular
value decomposition (t-SVD) framework has gained considerable attention owing to its …
value decomposition (t-SVD) framework has gained considerable attention owing to its …
Robust tensor decomposition via orientation invariant tubal nuclear norms
Aiming at recovering an unknown tensor (ie, multi-way array) corrupted by both sparse
outliers and dense noises, robust tensor decomposition (RTD) serves as a powerful pre …
outliers and dense noises, robust tensor decomposition (RTD) serves as a powerful pre …
Transformed low-rank parameterization can help robust generalization for tensor neural networks
Multi-channel learning has gained significant attention in recent applications, where neural
networks with t-product layers (t-NNs) have shown promising performance through novel …
networks with t-product layers (t-NNs) have shown promising performance through novel …
Recovery of corrupted data in wireless sensor networks using tensor robust principal component analysis
X Zhang, J He, Y Li, Y Chi… - IEEE Communications …, 2021 - ieeexplore.ieee.org
Due to the hardware and network conditions, data collected in Wireless Sensor Networks
usually suffer from loss and corruption. Most existing research works mainly consider the …
usually suffer from loss and corruption. Most existing research works mainly consider the …
Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data
This paper conducts a rigorous analysis for the problem of robust tensor completion, which
aims at recovering an unknown three-way tensor from incomplete observations corrupted by …
aims at recovering an unknown three-way tensor from incomplete observations corrupted by …
A hybrid norm for guaranteed tensor recovery
Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating
low-rankness in the spectral domain over other tensor decompositions (like Tucker …
low-rankness in the spectral domain over other tensor decompositions (like Tucker …
Tensor low-rank representation combined with consistency and diversity exploration
Y Kan, GF Lu, G Ji, Y Du - International Journal of Machine Learning and …, 2024 - Springer
In recent years, many tensor data processing methods have been proposed. Tensor low-
rank representation (TLRR) is a recently proposed tensor-based clustering method that has …
rank representation (TLRR) is a recently proposed tensor-based clustering method that has …
Robust high-order tensor recovery via nonconvex low-rank approximation
The latest tensor recovery methods based on tensor Singular Value Decomposition (t-SVD)
mainly utilize the tensor nuclear norm (TNN) as a convex surrogate of the rank function …
mainly utilize the tensor nuclear norm (TNN) as a convex surrogate of the rank function …
Online Tensor Max-Norm Regularization via Stochastic Optimization
T Wu - Transactions on Machine Learning Research - openreview.net
The advent of ubiquitous multidimensional arrays poses unique challenges for low-rank
modeling of tensor data due to higher-order relationships, gross noise, and large …
modeling of tensor data due to higher-order relationships, gross noise, and large …