Bayesian Tensor Decomposition for Signal Processing and Machine Learning

Lei Cheng, Cheng,Zhongtao Chen, Chen,Yik-Chung Wu, Wu

ISBN 10: 3031224396 / ISBN 13: 9783031224393

Published by Springer Nature B.V., 2023

About this Item

New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9783031224393

Bibliographic Details

Title: Bayesian Tensor Decomposition for Signal ...

Publisher: Springer Nature B.V.

Publication Date: 2023

Binding: PAP

Condition: New

About this title

Synopsis:

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

• 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.