Book chapter in “Tensors for Data Processing (1st Edition)”
Book chapter “A Riemannian approach to low-rank tensor learning” in Tensors for Data Processing
It is pleased to announce that our book chapter in “Tensors for Data Processing” has been published by Elsevier.
Tensors for Data Processing
- Editor: Yipeng Liu
- Paperback ISBN: 9780128244470
- Published: Nov. 1, 2021
- No. of pages: 596
- Abstract: (excerpt from the website of the book)
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.
- Our book chapter (4th chapter): A Riemannian approach to low-rank tensor learning
- Author: HK, Pratik Jawanpuria, Bamdev Mishra
- Code: Github