Tensors for Data Processing

Theory, Methods, and Applications

Yipeng Liu

• Publisher: Academic Press
• Year: 2021
• Print ISBN: 9780128244470
• eBook ISBN: 9780323859653
• Topic: Technology & Engineering
• Subtopic: Signals & Signal Processing
• Index: Technology & Engineering

Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This reference is ideal for students, researchers and industry developers who want to understand and use tensor-based data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear data structure loss that occurs in classical matrix-based data processing methods. This move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics and quantum chemistry. - Provides a complete reference on classical and state-of-the-art tensor-based methods for data processing - Includes a wide range of applications from different disciplines - Gives guidance for their application

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Preface
• Chapter 1 Tensor decompositions: computations, applications, and challenges
• Chapter 2 Transform-based tensor singular value decomposition in multidimensional image recovery
• Chapter 3 Partensor: A toolbox for parallel canonical polyadic decomposition
• Chapter 4 A Riemannian approach to low-rank tensor learning
• Chapter 5 Generalized thresholding for low-rank tensor recovery: approaches based on model and learning
• Chapter 6 Tensor principal component analysis
• Chapter 7 Tensors for deep learning theory: Analyzing deep learning architectures via tensorization
• Chapter 8 Tensor network algorithms for image classification
• Chapter 9 High-performance tensor decompositions for compressing and accelerating deep neural networks
• Chapter 10 Coupled tensor decompositions for data fusion
• Chapter 11 Tensor methods for low-level vision
• Chapter 12 Tensors for neuroimaging: A review on applications of tensors to unravel the mysteries of the brain
• Chapter 13 Tensor representation for remote sensing images
• Chapter 14 Structured tensor train decomposition for speeding up kernel-based learning
• Index