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

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

Brossura

ISBN 10: 303122440X ISBN 13: 9783031224409

Casa editrice: Springer Nature, 2024

Riassunto

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.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 303122440X
• ISBN 13: 9783031224409
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 196
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com