Stephen Roberts / Richard Everson (eds.)

Independent Component Analysis

Principles and Practice
Herausgeber: Everson, Richard; Roberts, Stephen

• Gebundenes Buch

Produktbeschreibung

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.

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.

Independent Component Analysis is an important tool for modelling and understanding empirical datasets consisting of mixtures of independent sources. This book contains papers by leading researchers on the subject. The theory is reviewed and many applications are described. Ideal for researchers and graduate students in the field.

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

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 352
• Erscheinungstermin: 18. August 2010
• Englisch
• Abmessung: 235mm x 157mm x 25mm
• Gewicht: 724g
• ISBN-13: 9780521792981
• ISBN-10: 0521792983
• Artikelnr.: 21597582

Inhaltsangabe

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.