Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …

Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …

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 …

This work proposes a novel approach for multiple time series forecasting. At first, multi-way
delay embedding transform (MDT) is employed to represent time series as low-rank block …

In this paper, a tensor-based joint channel parameter estimation and information symbol
detection scheme is developed for millimeter wave (mmWave) massive multiple-input …

High-dimensional image representation is a challenging task since data has the intrinsic low-
dimensional and shift-invariant characteristics. Currently, popular methods, such as tensor …

The synthesis of high-resolution (HR) hyperspectral image (HSI) by fusing a low-resolution
HSI with a corresponding HR multispectral image has emerged as a prevalent HSI super …

Since higher-order tensors are naturally suitable for representing multi-dimensional data in
real-world, eg, color images and videos, low-rank tensor representation has become one of …

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the
tensor in a frequency domain, has been found useful in solving low-rank tensor recovery …

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as
an essential yet challenging problem. In particular, since thetensor rank controls the …