Bayesian Tensor Decomposition for Signal Processing and Machine Learning (PDF)
Modeling, Tuning-Free Algorithms, and Applications
(Sprache: Englisch)
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...
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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.
Bibliographische Angaben
- Autoren: Lei Cheng , Zhongtao Chen , Yik-Chung Wu
- 2023, 1st ed. 2023, 183 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031224388
- ISBN-13: 9783031224386
- Erscheinungsdatum: 16.02.2023
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 3.55 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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