Table of Contents
Advances in Artificial Neural Systems
Volume 2012, Article ID 962105, 13 pages
Research Article

Measuring Non-Gaussianity by Phi-Transformed and Fuzzy Histograms

1400 Dirac Science Library, Florida State University, Tallahassee, FL 32306-4120, USA
2Department for Informatics, Research Unit for Database Systems, University of Munich, Oettingenstraße 67, 80538 Munich, Germany
3Klinikum rechts der Isar der TUM, Ismaninger Straße 22, 81675 Munich, Germany
4Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

Received 14 February 2012; Accepted 1 April 2012

Academic Editor: Juan Manuel Gorriz Saez

Copyright © 2012 Claudia Plant et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Independent component analysis (ICA) is an essential building block for data analysis in many applications. Selecting the truly meaningful components from the result of an ICA algorithm, or comparing the results of different algorithms, however, is nontrivial problems. We introduce a very general technique for evaluating ICA results rooted in information-theoretic model selection. The basic idea is to exploit the natural link between non-Gaussianity and data compression: the better the data transformation represented by one or several ICs improves the effectiveness of data compression, the higher is the relevance of the ICs. We propose two different methods which allow an efficient data compression of non-Gaussian signals: Phi-transformed histograms and fuzzy histograms. In an extensive experimental evaluation, we demonstrate that our novel information-theoretic measures robustly select non-Gaussian components from data in a fully automatic way, that is, without requiring any restrictive assumptions or thresholds.