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arXiv · 1802.05550

ICA based on Split Generalized Gaussian

P. Spurek·P. Rola·J. Tabor·A. Czechowski
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Abstract

Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. Most popular ICA methods use kurtosis as a metric of 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. \end{abstract}

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P. Spurek, P. Rola, J. Tabor, A. Czechowski. 2018-02-14. ICA based on Split Generalized Gaussian. https://arxiv.org/abs/1802.05550

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