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BAYESIAN TENSOR DECOMPOSITION FOR SIGNAL PROCESSING AND MACHINE LEARNING : modeling,... tuning-free algorithms and applications by Lei Cheng; Zhongtao Chen; Yik-Chung Wu is a nonfiction available to read on EtoBox.

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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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Author

Lei Cheng; Zhongtao Chen; Yik-Chung Wu

Publisher

Springer International Publishing Springer

Published

2023

Language

EN

ISBN

9783031224386

Category

nonfiction

Subjects

Engineering, Mathematics, Stem