Estimating source kurtosis directly from observation data for ICA

Authors:
Jianwei Wu
Affiliations:
Department of Information and Calculation Science, School of Science, The Central University of Nationalities, Beijing 100081, PR China
Venue:
Signal Processing
Year:
2011

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Abstract

Based on the fourth order blind identification (FOBI) in ICA methods, this paper shows that kurtosis of each independent source component can be estimated with eigenvalues of two relative matrices directly from observation data. With the new approach, the total number of super-Gaussian components in noiseless observation data can be directly calculated before any de-mixing algorithm is performed. As an application, a new switching criterion for the extended infomax algorithm is presented. Experimental results demonstrate the effectiveness of the kurtosis estimation algorithm and the new alternative switching criterion.