Independent Component Analysis
Principles and Practice

Series of edited papers on Independent Component Analysis, containing theory and applications.

Stephen Roberts (Edited by), Richard Everson (Edited by)

9780521792981, Cambridge University Press

Hardback, published 1 March 2001

352 pages
23.6 x 16 x 2.4 cm, 0.7 kg

'… is ideal for graduate students and researchers in the field.' Zentralblatt MATH

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.

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.

Subject Areas: Electronics & communications engineering [TJ], Probability & statistics [PBT]