Overview
- Studies the latest developments of Bayesian tensor decompositions
- Provides numerous applications of structured tensor canonical polyadic decompositions
- Moves through the topics in a well-structured, pedagogical way
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About this book
This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including
The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.
Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
- blind source separation;
- social network mining;
- image and video processing;
- array signal processing; and,
- wireless communications.
The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.
Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
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Keywords
Table of contents (9 chapters)
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Front Matter
Authors and Affiliations
Bibliographic Information
Book Title: Bayesian Tensor Decomposition for Signal Processing and Machine Learning
Book Subtitle: Modeling, Tuning-Free Algorithms, and Applications
Authors: Lei Cheng, Zhongtao Chen, Yik-Chung Wu
DOI: https://doi.org/10.1007/978-3-031-22438-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-22437-9Published: 17 February 2023
Softcover ISBN: 978-3-031-22440-9Published: 17 February 2024
eBook ISBN: 978-3-031-22438-6Published: 16 February 2023
Edition Number: 1
Number of Pages: X, 183
Number of Illustrations: 20 b/w illustrations, 41 illustrations in colour
Topics: Signal, Image and Speech Processing, Numerical Analysis, Bayesian Inference