Independent Component Analysis
Principles and Practice


Stephen Roberts and Richard Everson.


Stephen Roberts, Richard Everson, Aapo Hyvärinen, Hagai Attias, Juha Karhunen, Lucas Parra, Clay Spence, Jean-François Cardoso, Dinh-Tuan Pham, Michael Zibulevsky, Barak Pearlmutter, Pau Bofill, Pavel Kisilev, James Miskin, David MacKay, Te-Won Lee, Michael S. Lewicki, Mark Girolami, William Penny

Published by Cambridge University Press



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.


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