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 …

In this paper, we study T-eigenvalues of third-order tensors. Definitions of the T-eigenvalues
and Hermitian tensors are proposed. We present a commutative tensor family. We prove …

Multi-dimensional images, such as color images and multi-spectral images (MSIs), are
highly correlated and contain abundant spatial and spectral information. However, real …

In this paper, we investigate the tensor similarity and propose the T-Jordan canonical form
and its properties. The concepts of the T-minimal polynomial and the T-characteristic …

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to
predict missing entries from partial observations. However, existing methods often make the …

Nowadays, multi-dimensional data (tensor data) have shown their capability of preserving
multilinear structures. Due to the measuring error or other non-human factors, these data …

In this paper, we study the problem of low-rank tensor recovery from limited sampling with
noisy observations for third-order tensors. A tensor nuclear norm method based on a convex …

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 …

Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A
handful of completion methods have been proposed, among which the tensor-based models …

In recent times, low-rank tensor completion (LRTC), which involves completing missing
entries in partially observed tensors, is attracting significant attention from researchers …