1732-3916 ICA based on Split Generalized Gaussian Spurek Przemysław author Rola Przemysław author Tabor Jacek author Czechowski Aleksander author Bedychaj Andrzej author Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland Uniwersytet Ekonomiczny w Krakowie 0262te083 Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland Delft University of Technology, Delft, The Netherlands Correspondence to: Przemysław Spurek przemyslaw.spurek@uj.edu.pl Correspondence to: Przemysław Rola Correspondence to: Jacek Tabor jacek.tabor@uj.edu.pl Correspondence to: Aleksander Czechowski czechows@ii.uj.edu.pl Correspondence to: Andrzej Bedychaj 30 09 2019 Volume 28 2019 25 47 Copyright © 2019 2019 <p>Independent Component Analysis (ICA) is a method for searching the linear transformation that minimizes the statistical dependence between its components. Most popular ICA methods use kurtosis as a metric of independence (non-Gaussianity) to maximize, such as FastICA and JADE. However, their assumption of fourth-order moment (kurtosis) may not always be satisfied in practice. One of the possible solution is to use third-order moment (skewness)  instead of kurtosis, which was applied in ICA_SG and EcoICA. In this paper we present a competitive approach to ICA based on the Split Generalized Gaussian distribution (SGGD), which is well adapted to heavy-tailed as well as asymmetric data. Consequently, we obtain a method which works better than the classical approaches, in both cases: heavy tails and non-symmetric data.</p> 1 2 [1] Beckmann, C.F., Smith, S.M., Probabilistic independent component analysis for functional magnetic resonance imaging. Medical Imaging, IEEE Transactions on, 2004, 23(2), pp. 137–152. 3 [2] Beckmann, C.F., Smith, S.M., Tensorial extensions of independent component analysis for multisubject fmri analysis. 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