Independent Component Analysis
Principles and Practice
Published on 1. March 2001
Book
Hardback
352 pages
978-0-521-79298-1 (ISBN)
Description
Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. 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, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.
Reviews / Votes
'The book is intended to be a self-contained introduction and overview of this important development and it appears to meet the requirement admirably.' Alex M. Andrew, Robotica '... is ideal for graduate students and researchers in the field.' Zentralblatt MATHMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 25 mm
Weight
722 gr
ISBN-13
978-0-521-79298-1 (9780521792981)
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Schweitzer Classification
Persons
Content
1. Introduction Stephen Roberts and Richard Everson; 2. Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity Aapo Hyvaerinen; 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-Francois 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.