Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

Low-rank tensor representations can provide highly compressed approximations of
functions. These concepts, which essentially amount to generalizations of classical …

Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …

This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …

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 …

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

The tensor-on-tensor regression can predict a tensor from a tensor, which generalizes most
previous multilinear regression approaches, including methods to predict a scalar from a …

The tensor-train (TT) decomposition expresses a tensor in a data-sparse format used in
molecular simulations, high-order correlation functions, and optimization. In this paper, we …

Low-rank tensor completion methods have been advanced recently for modeling sparsely
observed data with a multimode structure. However, low-rank priors may fail to interpret the …

This article introduces two Tensor Network-based iterative algorithms for the identification of
high-order discrete-time nonlinear multiple-input multiple-output (MIMO) Volterra systems …