ICA based on Split Generalized Gaussian

2018/02/14 by P. Spurek, P. Rola, Spurek, P. +5
Chemistry · Computer Science · #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neural Networks and Applications #Spectroscopy and Chemometric Analyses

paper · pdf · doi:10.48550/arxiv.1802.05550

openalex publication_date 2018/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 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. \endabstract

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