Название: Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and ApplicationsАвтор: Lei Cheng, Zhongtao Chen, Yik-Chung Wu
Издательство: Springer
Год: 2023
Страниц: 189
Язык: английский
Формат: pdf (true), epub
Размер: 32.0 MB
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. This book starts by reviewing the basics and classical algorithms for tensor decompositions, and then introduces their common challenge on rank determination (Chap. 1). To overcome this challenge, this book develops models and algorithms under the Bayesian sparsity-aware learning framework, with the philosophy and key results elaborated in Chap. 2. Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
Название: Metal-Air Batteries: Principles, Progress, and Perspectives
