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

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

Autor Lei Cheng, Zhongtao Chen, Yik-Chung Wu
en Limba Engleză Hardback – 17 feb 2023
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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Specificații

ISBN-13: 9783031224379
ISBN-10: 303122437X
Pagini: 183
Ilustrații: X, 183 p. 61 illus., 41 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.48 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Studies the latest developments of Bayesian tensor decompositions Provides numerous applications of structured tensor canonical polyadic decompositions Moves through the topics in a well-structured, pedagogical way