Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications

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

ISBN 10: 303122437X ISBN 13: 9783031224379

Editorial: Springer, 2023

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa dura

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Detalles bibliogr�ficos

T�tulo: Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications
Editor: Springer
Fecha de publicaci�n: 2023
Idioma: Ingl�s
ISBN 10: 303122437X
ISBN 13: 9783031224379
Encuadernaci�n: Encuadernaci�n de tapa dura
Estado: New

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Sinopsis

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.

De la contraportada

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.

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