Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor
completion problem, which has achieved state-of-the-art performance on image and video …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

In tensor completion, the goal is to fill in missing entries of a partially known tensor under a
low-rank constraint. We propose a new algorithm that performs Riemannian optimization …

In this paper, we study the problem of recovering a tensor with missing data. We propose a
new model combining the total variation regularization and low-rank matrix factorization. A …

In recent years, low-rank tensor completion (LRTC) problems have received a significant
amount of attention in computer vision, data mining, and signal processing. The existing …

Tensor completion aims to reconstruct a high-dimensional data set where the vast majority
of entries is missing. The assumption of low-rank structure in the underlying original data …

We consider the problem of fitting a low rank tensor A∈R^\mathcalI, \mathcalI={1,...,n\}^d, to
a given set of data points {M_i∈R∣i∈P\}, P⊂\mathcalI. The low rank format under …

Tensor completion (TC) refers to restoring the missing entries in a given tensor by making
use of the low-rank structure. Most existing algorithms have excellent performance in …

Low-rank tensor completion (LRTC) has successfully been applied to a wide range of real-
world problems. Despite the broad, successful applications, existing LRTC methods may …

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 …