ICA based on the data asymmetry
Statistics Theory
2017-02-01 v1 Statistics Theory
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.
Cite
@article{arxiv.1701.09160,
title = {ICA based on the data asymmetry},
author = {Przemysław Spurek and Jacek Tabor and Przemysław Rola and Michał Ociepka},
journal= {arXiv preprint arXiv:1701.09160},
year = {2017}
}