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Stone, J.V. (2004)Independent Component Analysis: A Tutorial Introduction

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"Independent component analysis (ICA) is becoming an increasingly important tool for analysing large data sets. In essence, ICA separates an observed set of signals mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock prediction.

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."

Additional Nematrian Commentary

We have used material in this book to develop ICA algorithms made available through the Nematrian website.

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