Bayesian Tensor Decomposition for Signal Processing and Machine Learning

Cheng

ISBN 10: 303122437X / ISBN 13: 9783031224379

Published by Springer, 2023

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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.

From the Back Cover:

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.

Bibliographic Details

Title: Bayesian Tensor Decomposition for Signal ...
Publisher: Springer
Publication Date: 2023
Binding: Hardcover
Condition: Brand New