Independent Component Analysis: Principles and Practice

Independent Components Analysis (ICA) is an important tool for modeling and understanding empirical data sets. Belonging to the class of general linear models, it is a method of separating out independent sources from linearly mixed data. 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 and includes an extensive introduction to ICA. It reviews the major theoretical bases from a modern perspective, surveys current developments, and describes many case studies of applications in detail. Applications include biomedical examples, signal and image denoising, and mobile communications. The book discusses ICA within the framework of general linear models, but it also compares it to other paradigms such as neural network and graphical modeling methods.

Independent Components Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from mixed data and provides a better decomposition than other well-known models.This self-contained book contains papers by leading researchers in the field. The theory is reviewed, current developments are surveyed and many applications are described. The latter include biomedical examples, signal denoising and mobile communications. The book is ideal for graduate students and researchers in signal and image processing, data analysis and information theory.