%0 Journal Article
%T ICA based on Split Generalized Gaussian
%A Spurek, Przemysław
%A Rola, Przemysław
%A Tabor, Jacek
%A Czechowski, Aleksander
%A Bedychaj, Andrzej
%J Schedae Informaticae
%V 2019
%R 10.4467/20838476SI.19.002.14379
%N Volume 28
%P 25-47
%K ICA, Split Normal distribution, skewness, kurtosis
%@ 1732-3916
%D 2019
%U https://ejournals.eu/en/journal/schedae-informaticae/article/ica-based-on-split-generalized-gaussian
%X 
	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.