In this paper, we present the definition of generalized tensor function according to the tensor
singular value decomposition (T-SVD) based on the tensor T-product. Also, we introduce the …

Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has
been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers …

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 …

Phase-noise is a system impairment caused by the mismatch between the oscillators at the
transmitter and the receiver. In OFDM systems, this induces inter-carrier-interference (ICI) by …

The T-product for third-order tensors has been used extensively in the literature. In this
paper, we first introduce first-order and second-order T-derivatives for the multi-variable real …

Aiming at recovering a signal tensor from its mixture with outliers and noises, robust tensor
decomposition (RTD) arises frequently in many real-world applications. Recently, the low …

This paper proposes two tensor-based receivers for multiple-input multiple-output (MIMO)
multi-relaying systems capable of jointly estimating the channels and symbols in a semi …

In this paper, we mainly develop the well-known vector and matrix polynomial extrapolation
methods in tensor framework. To this end, some new products between tensors are defined …

In this work, we investigate the tensor inequalities in the tensor t-product formalism. The
inequalities involving tensor power are proved to hold similarly as standard matrix …

Many restoration methods use the low-rank constraint of high-dimensional image signals to
recover corrupted images. These signals are usually represented by tensors, which can …