  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}
