Tensor factorization for low-rank tensor completion
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 …
completion problem, which has achieved state-of-the-art performance on image and video …
Tensor completion algorithms in big data analytics
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 …
observed tensors. Due to the multidimensional character of tensors in describing complex …
Low-rank tensor completion by Riemannian optimization
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 …
low-rank constraint. We propose a new algorithm that performs Riemannian optimization …
Tensor completion using total variation and low-rank matrix factorization
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 …
new model combining the total variation regularization and low-rank matrix factorization. A …
Trace norm regularized CANDECOMP/PARAFAC decomposition with missing data
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 …
amount of attention in computer vision, data mining, and signal processing. The existing …
Riemannian optimization for high-dimensional tensor completion
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 …
of entries is missing. The assumption of low-rank structure in the underlying original data …
Generalized higher-order orthogonal iteration for tensor decomposition and completion
Low-rank tensor estimation has been frequently applied in many real-world problems.
Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may …
Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may …
Robust tensor completion via capped Frobenius norm
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 …
use of the low-rank structure. Most existing algorithms have excellent performance in …
Variants of alternating least squares tensor completion in the tensor train format
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 …
a given set of data points {M_i∈R∣i∈P\}, P⊂\mathcalI. The low rank format under …
Noisy low-tubal-rank tensor completion
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 …
often where the acquired data suffers from miss values and noise. Recently, models based …