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Title
Bayesian tensor decomposition for signal processing and machine learning : modeling, tuning-free algorithms and applications / Lei Cheng, Zhongtao Chen, Yik-Chung Wu.
ISBN
9783031224386 (electronic bk.)
3031224388 (electronic bk.)
303122437X
9783031224379
Published
Cham, Switzerland : Springer, 2023.
Language
English
Description
1 online resource (180 pages) : illustrations (black and white, and colour).
Item Number
10.1007/978-3-031-22438-6 doi
Call Number
TK5102.9
Dewey Decimal Classification
621.38220151954
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
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.
