Independent Component Analysis : Principles and Practice
Editorial Reviews
Review
'The book is intended to be a self-contained introduction and overview of this important development and it appears to meet the requirement admirably.' Alex M. Andrew, Robotica
'... is ideal for graduate students and researchers in the field.' Zentralblatt MATH
Book Description
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 Component Analysis : Principles and Practice
Independent Component Analysis: Principles and Practice,Stephen Roberts,Richard Everson,Cambridge University Press,0521792983,Applied,Artificial Intelligence - General,Computers,Mathematics,Multivariate Analysis,Neural Computing,Neural Networks,Neural networks (Computer scie,Neural networks (Computer science),Probability & Statistics - General,Science/Mathematics,Communications engineering / telecommunications,Mathematics / Statistics,Probability & statistics
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