Bayesian Tensor Decomposition for Signal Processing and Machine Learning

Modeling, Tuning-Free Algorithms, and Applications
1. Auflage 2023
ISBN: 978-3-031-22437-9
Verlag: Springer International Publishing

Buch, Englisch, 183 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 501 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Cheng, Lei

Wu, Yik-Chung

Chen, Zhongtao

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz

Inhaltsverzeichnis

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.