Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

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 …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone
of traditional machine learning and data analysis, tensor methods have been gaining …

Abstract Laser-Based Additive Manufacturing (LBAM) is a fabrication process that is a key
aspect of Industry 4.0, which aims to employ many sensors for continuous process control …

Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

Handbook of Regression Methods concisely covers numerous traditional, contemporary,
and nonstandard regression methods. The handbook provides a broad overview of …

In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …

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

Recent years have seen an explosion of activity in the field of functional data analysis (FDA),
in which curves, spectra, images and so on are considered as basic functional data units. A …