Low-rank tensor completion aims to recover the missing entries of multi-way data, which has
become popular and vital in many fields such as signal processing and computer vision. It …

Many classical data processing techniques rely on the representation and computation of
vector and matrix forms, where the vectorization or matricization is often employed on …

Tensor completion recovers a multi-dimensional array from a limited number of
measurements. Using the recently proposed tensor ring (TR) decomposition, in this paper …

Dynamic magnetic resonance imaging (DMRI) often requires a long time for measurement
acquisition, and it is a crucial problem about the enhancement of reconstruction quality from …

Tensor singular value decomposition (t-SVD) has recently become increasingly popular for
tensor recovery under partial and/or corrupted observations. However, the existing-SVD …

Convolutional neural networks (CNNs) based on the discriminative learning model have
been widely used for image denoising. In this study, a feed‐forward denoising CNN …

In recent years, most of the studies have shown that the generalized iterated shrinkage
thresholdings (GISTs) have become the commonly used first-order optimization algorithms …

Anomalous traffic detection is a vital task in advanced Internet supervision and maintenance.
To detect anomalies accurately, various data representations, such as vectors, matrices, and …

Multiway data-related learning tasks pose a huge challenge to the traditional regression
analysis techniques due to the existence of multidirectional relatedness. Simply vectorizing …

This paper presents a novel probability-weighted tensor robust principal component
analysis (TRPCA) method based on CANDECOMP/PARAFAC decomposition (CPD) for …