Measuring Non-Gaussianity by Phi-Transformed and Fuzzy Histograms. (4th June 2012)

• Record Type: Journal Article
• Authors: Plant, Claudia; Thai, Son Mai; Shao, Junming; Theis, Fabian J.; Meyer-Baese, Anke; Böhm, Christian
• Other Names: Saez Juan Manuel Gorriz Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2012)
• Publication Date: 2012-06-04
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2012/962105
• Languages: English
• ISSNs: 1687-7594

Abstract

Abstract : 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.