Independent Component Analysis: Principles and Practiceby Stephen Roberts
Pub. Date: 03/28/2001
Publisher: Cambridge University Press
Independent Components Analysis (ICA) is an important tool for modeling and understanding empirical data sets. Belonging to the class of general linear models, it is a method of separating out independent sources from linearly mixed data. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book… See more details below
Independent Components Analysis (ICA) is an important tool for modeling and understanding empirical data sets. Belonging to the class of general linear models, it is a method of separating out independent sources from linearly mixed data. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field and includes an extensive introduction to ICA. It reviews the major theoretical bases from a modern perspective, surveys current developments, and describes many case studies of applications in detail. Applications include biomedical examples, signal and image denoising, and mobile communications. The book discusses ICA within the framework of general linear models, but it also compares it to other paradigms such as neural network and graphical modeling methods.
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Table of Contents1. 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.
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