ICA based on asymmetry. (July 2017)

• Record Type: Journal Article
• Authors: Spurek, P.; Tabor, J.; Rola, P.; Ociepka, M.
• Is Part Of: Pattern recognition. Volume 67(2017:Jul.)
• Page Start: 230
• Page End: 244
• Publication Date: 2017-07
• Subjects: ICA -- Split Normal distribution -- Skewness; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.02.019
• Languages: English
• ISSNs: 0031-3203

Abstract

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.