AH igh-D imensional and I ncomplete (HDI) tensor is frequently encountered in a big data-
related application concerning the complex dynamic interactions among numerous entities …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

In this technical article, to seek the optimal solution to time-dependent nonlinear optimization
subject to linear inequality and equality constraints (TDNO-IEC), the gradient-based …

The main aim of this paper is to develop a framelet representation of the tensor nuclear norm
for third-order tensor recovery. In the literature, the tensor nuclear norm can be computed by …

Hyperspectral image (HSI) super-resolution is a hot topic in remote sensing and computer
vision. Recently, tensor analysis has been proven to be an efficient technology for HSI …

Non-negative Tucker decomposition (NTD) is one of the most popular techniques for tensor
data representation. To enhance the representation ability of NTD by multiple intrinsic cues …

The recent popular tensor tubal rank, defined based on tensor singular value decomposition
(t-SVD), yields promising results. However, its framework is applicable only to three-way …

Objective. Epilepsy is one of the most common brain disorders. For epilepsy diagnosis or
treatment, the neurologist needs to observe epileptic spikes from electroencephalography …

Low-rank tensor completion plays an important role in many applications such as image
processing, computer vision, and machine learning. A widely used convex relaxation of this …

Tensor-ring (TR) decomposition has recently attracted considerable attention in solving the
low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding …