Independent Component Analysis
Independent Component Analysis
$149.00
USDIndependent 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.
- Self-contained introduction to independent component analysis (ICA)
- Many applications treated by leading experts
- Overview of future directions
Reviews & endorsements
"...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
Product details
March 2001Hardback
9780521792981
352 pages
236 × 160 × 24 mm
0.7kg
Available
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.
