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26th May 2009 — Arthur, ICA Tutorial
This week Arthur Gretton will give a tutorial on independent component analysis (ICA).
Introduction to Independent Component Analysis
Abstract:
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.
Dilan Görür