ICA based on asymmetry. (July 2017) - Spurek, P.; Tabor, J.; Rola, P.; Ociepka, M.
Highlights: We build a new approach to ICA which is based on the data asymmetry. Instead of densities with heavy tails, we use asymmetric ones - Split Gaussians. We verified our approach on images, sound and EEG data. In the case of source signal reconstructing our approach gives better results. 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.