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Measuring non-Gaussianity by phi-transformed and fuzzy histograms.
Adv. Artif. Neural Syst. 2012:962105 (2012)
Publ. Version/Full Text Volltext
DOI
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.
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
1687-7594
e-ISSN
1687-7608
Quellenangaben
Volume: 2012,
Article Number: 962105
Publisher
Hindawi
Non-patent literature
Publications
Reviewing status
Peer reviewed