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Bayesian tensor decomposition for si...

Cheng, Lei.

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Bayesian tensor decomposition for signal processing and machine learning = modeling, tuning-free algorithms, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian tensor decomposition for signal processing and machine learning/ by Lei Cheng, Zhongtao Chen, Yik-Chung Wu.
其他題名:	modeling, tuning-free algorithms, and applications /
作者:	Cheng, Lei.
其他作者:	Chen, Zhongtao.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	x, 183 p. :ill., digital ;24 cm.
內容註:	Tensor decomposition: Basics, algorithms, and recent advances -- Bayesian learning for sparsity-aware modeling -- Bayesian tensor CPD: Modeling and inference -- Bayesian tensor CPD: Performance and real-world applications -- When stochastic optimization meets VI: Scaling Bayesian CPD to massive data -- Bayesian tensor CPD with nonnegative factors -- Complex-valued CPD, orthogonality constraint and beyond Gaussian noises -- Handling missing value: A case study in direction-of-arrival estimation -- From CPD to other tensor decompositions.
Contained By:	Springer Nature eBook
標題:	Signal processing - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-031-22438-6
ISBN:	9783031224386

Bayesian tensor decomposition for signal processing and machine learning = modeling, tuning-free algorithms, and applications /
Cheng, Lei.

Bayesian tensor decomposition for signal processing and machine learningmodeling, tuning-free algorithms, and applications /[electronic resource] :by Lei Cheng, Zhongtao Chen, Yik-Chung Wu. - Cham :Springer International Publishing :2023. - x, 183 p. :ill., digital ;24 cm.

Tensor decomposition: Basics, algorithms, and recent advances -- Bayesian learning for sparsity-aware modeling -- Bayesian tensor CPD: Modeling and inference -- Bayesian tensor CPD: Performance and real-world applications -- When stochastic optimization meets VI: Scaling Bayesian CPD to massive data -- Bayesian tensor CPD with nonnegative factors -- Complex-valued CPD, orthogonality constraint and beyond Gaussian noises -- Handling missing value: A case study in direction-of-arrival estimation -- From CPD to other tensor decompositions.

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.

ISBN: 9783031224386

Standard No.: 10.1007/978-3-031-22438-6doiSubjects--Topical Terms:

649787
Signal processing
--Statistical methods.

LC Class. No.: TK5102.9 / .C44 2023

Dewey Class. No.: 621.38220151954

Bayesian tensor decomposition for signal processing and machine learning = modeling, tuning-free algorithms, and applications /
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520 $a 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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