Synopses & Reviews
Series of edited papers on Independent Component Analysis, containing theory and applications.
Review
"...a highly technical book that is fascinating...There are a lot of deep ideas in this book and, as such, experts in ICA will want to have it at their disposal. No doubt this book would be a wonderful resource for a graduate student about to embark on the long pursuit of a thesis in the field." Technometrics
Synopsis
Series of edited papers on Independent Component Analysis, containing theory and applications.
Synopsis
Independent Components Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from mixed data and provides a better decomposition than other well-known models.This self-contained book contains papers by leading researchers in the field. The theory is reviewed, current developments are surveyed and many applications are described. The latter include biomedical examples, signal denoising and mobile communications. The book is ideal for graduate students and researchers in signal and image processing, data analysis and information theory.
Table of Contents
1. Introduction Stephen Roberts and Richard Everson; 2. Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity Aapo Hyvärinen; 3. ICA, graphical models and variational methods Hagai Attias; 4. Nonlinear independent component analysis Juha Karhunen; 5. Separation of non-stationary natural signals Lucas Parra and Clay Spence; 6. Separation of non-stationary sources: algorithms and performance Jean-François Cardoso and Dinh-Tuan Pham; 7. Blind source separation by sparse decomposition in a signal dictionary Michael Zibulevsky, Barak Pearlmutter, Pau Bofill and Pavel Kisilev; 8. Ensemble learning for blind source separation James Miskin and David MacKay; 9. Image processing methods using ICA mixture models Te-Won Lee and Michael S. Lewicki; 10. Latent class and trait models for data classification and visualisation Mark Girolami; 11. Particle filters for non-stationary ICA Richard Everson and Stephen Roberts; 12. ICA: model order selection and dynamic source models William Penny, Stephen Roberts and Richard Everson.