Algorithms, Analysis and Applications
Buch, Englisch, 202 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 482 g
ISBN: 978-3-032-29622-1
Verlag: Springer
This book provides a comprehensive and in-depth exploration of adaptive filtering algorithms based on the Information Theoretic Learning (ITL). As a powerful alternative to traditional second-order statistical methods, ITL-based adaptive filtering algorithms are particularly effective in dealing with non-Gaussian noise. The book systematically introduces core ITL criteria such as minimum error entropy and maximum correntropy and extends these principles to the field of multidimensional signal processing and nonlinear adaptive filtering, demonstrating their effectiveness through modeling real-world signals like wind speed and temperature. In addition to single-node filtering, this book thoroughly investigates distributed adaptive filtering, addressing collaborative learning across networked systems. It further integrates graph signal processing, allowing for efficient modeling and analysis of signals defined on irregular or structured domains. Together, these contributions showcase ITL as a unified and powerful learning framework, advancing adaptive filtering theory and methodology across linear, nonlinear, distributed, and graph-based signal processing environments.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction.- Maximum Correntropy Criterion .- Decorrelation Recursive Maximum Correntropy Criterion.- Combinatoric Correntropy Criterion.- Fast Minimum Error Entropy with Fiducial Points.- Asymmetric Correntropy Criterion.- Hybrid Geometric Algebra Correntropy Criterion.- Kernel Generalized Maximum Correntropy with Variable Centers.- Diffusion Kernel Generalized Maximum Correntropy with Variable Centers.- Nonlinear Graph Diffusion Maximum Mixture Correntropy.- Conclusion.
