ゲノム情報科学研究教育機構  アブストラクト
Date Oct 23, 2015
Speaker Aapo Hyvarinen, Professor, University of Helsinki, Finland
Title Independent component analysis: recent advances
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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