Date |
Oct 23, 2015 |
Speaker |
Aapo Hyvarinen, Professor, University of Helsinki, Finland
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Title |
Independent component analysis: recent advances
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Abstract |
Independent component analysis is a probabilistic method for learning
a linear transform of a random vector. The goal is to find components
which are maximally independent and non-Gaussian (non-normal). Its
fundamental difference to classical multivariate statistical methods
is in the assumption of non-Gaussianity, which enables the
identification of original, underlying components, in contrast to
classical methods.
In this talk, I provide an overview to the theory of ICA, as well as
an overview of recent developments in the theory. The main recent
topics are: testing independent components, analysing multiple data
sets (three-way data), analysis of causal relations, modelling
dependencies between the components, and improved methods for
estimating the basic model.
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