Independent Component Analysis: A Tutorial Introduction
Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal 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...
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A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples.
IIndependent component analysis and blind source separation11Overview of independent component analysis52Strategies for blind source separation13IIThe geometry of mixtures193Mixing and unmixing214Unmixing using the inner product315Independence and probability density functions51IIIMethods for blind source separation696Projection pursuit717Independent component analysis798Complexity pursuit1119Gradient ascent11910Principal component analysis and factor analysis129IVApplications13711Applications of ICA139VAppendices149AA vector matrix tutorial151BProjection pursuit gradient ascent157CProjection pursuit : stepwise separation of sources163DICA gradient ascent165EComplexity pursuit gradient ascent173FPrincipal component analysis for preprocessing data179GIndependent component analysis resources183
