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J. V. Stone. Independent Component Analysis: A Tutorial Introduction. MIT Press, 2004. ISBN: 0262693151. [Contents]

From Synopsis: In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings, and describes how ICA based on the key observation that different physical processes generate outputs which are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working MatLab computer code.

E. Oja, A. Hyvarinen, J. Karhunen. Independent Component Analysis. John Wiley & Sons, 2001. ISBN: 047140540X

Synopsis: A comprehensive introduction to ICA for students and practitioners Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more. Independent Component Analysis is divided into four sections that cover: General mathematical concepts utilized in the book The basic ICA model and its solution Various extensions of the basic ICA model Real-world applications for ICA models Authors Hyvarinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.

M. Girolami (Ed). Advances in Independent Component Analysis. Springer-Verlag UK, 2000. ISBN: 1852332638 [Amazon]

Synopsis: Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

A. Cichocki, S.-I. Amari. Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley and Sons, 2002. ISBN: 0471607916. [Amazon]

Synopsis: A reference on the important and rapidly developing field of blind and semi blind signal processing. Features of the text include: analysis of development in adaptive structures and associated unsupervised learning algorithms; discussion of effective computer simulation programmes; discussion of perspectives for future research in this area.


This page updated 09-Jan-2007