Low rank tensor completion for multiway visual data
Tensor completion recovers missing entries of multiway data. The missing of entries could
often be caused during the data acquisition and transformation. In this paper, we provide an …
often be caused during the data acquisition and transformation. In this paper, we provide an …
A novel approach to large-scale dynamically weighted directed network representation
A d ynamically w eighted d irected n etwork (DWDN) is frequently encountered in various big
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
Learning a low tensor-train rank representation for hyperspectral image super-resolution
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion
Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
The twist tensor nuclear norm for video completion
In this paper, we propose a new low-rank tensor model based on the circulant algebra,
namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor …
namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor …
Bayesian low rank tensor ring for image recovery
Low rank tensor ring based data recovery can recover missing image entries in signal
acquisition and transformation. The recently proposed tensor ring (TR) based completion …
acquisition and transformation. The recently proposed tensor ring (TR) based completion …
Enhanced sparsity prior model for low-rank tensor completion
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-
valued data lies in the global subspace. The so-called global sparsity prior is measured by …
valued data lies in the global subspace. The so-called global sparsity prior is measured by …
Reweighted tensor factorization method for SAR narrowband and wideband interference mitigation using smoothing multiview tensor model
For the interference suppression problem on synthetic aperture radar (SAR) systems,
traditional methods have focused on how to remove one kind of interference through …
traditional methods have focused on how to remove one kind of interference through …
Low CP rank and tucker rank tensor completion for estimating missing components in image data
Tensor completion recovers missing components of multi-way data. The existing methods
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …
use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank in low-rank tensor …