26th May 2009 — Arthur, ICA Tutorial

This week Arthur Gretton will give a tutorial on independent component analysis (ICA).

Introduction to Independent Component Analysis


Independent component analysis (ICA) is a technique for extracting underlying sources of information from linear mixtures of these sources, based only on the assumption that the sources are independent of each other. To illustrate the idea, we might be in a room containing several people (the sources) talking simultaneously, with microphones picking up multiple conversatisons at once (the mixtures), and we might wish to automatically recover the original separate conversations from these mixtures. More broadly, ICA is used in a very wide variety of applications, including signal extraction from EEG, image processing, bioinformatics, and economics. I will present an introduction to ICA, which includes the maximum likelihood approach, the case where fixed nonlinearities are used as heuristics for source extraction, some more modern information theoretic approaches, and a kernel-based method. I will also cover two optimization strategies, and provide a comparison of the various approaches on benchmark data, to reveal the strengths and failure modes of different ICA algorithms.