Title: ICA based on the data asymmetry,
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 of existing methods are based on the minimization of the
function of fourth-order moment (kurtosis). Skewness (third-order moment) has
received much less attention.
In this paper we present a competitive approach to ICA based on the Split
Gaussian distribution, which is well adapted to asymmetric data. Consequently,
we obtain a method which works better than the classical approaches, especially
in the case when the underlying density is not symmetric, which is a typical
situation in the color distribution in images.