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 …

Robust low-rank tensor completion (RTC) problems have received considerable attention in
recent years such as in signal processing and computer vision. In this paper, we focus on …

In many applications of multi-dimensional signal processing, noisy tensor completion arises
often where the acquired data suffers from miss values and noise. Recently, models based …

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 …

Aiming at recovering a signal tensor from its mixture with outliers and noises, robust tensor
decomposition (RTD) arises frequently in many real-world applications. Recently, the low …

Real multi-way data may suffer from missing entries, noise and outliers simultaneously. The
recently proposed tubal nuclear norm (TNN) has shown its superiority in tensor completion …

Low-rank tensor recovery faces challenges in accurately defining the low-rankness of a
tensor. Most existing definitions typically focus on one domain alone—either the original or …

Unlike traditional tensor decompositions which model low-rankness in the original domain,
the recently proposed tensor* L-Singular Value Decomposition (* L-SVD) casts a new light …

The recently proposed tensor tubal rank has been witnessed to obtain extraordinary success
in real-world tensor data completion. However, existing works usually fix the transform …

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 …