Independent Component Analysis

Independent component analysis (ICA) is a multivariate data analysis method that, given a linear mixture of statistical independent sources, recovers these components by producing an unmixing matrix. Stemming from a more general problem called blind source separation (BSS), ICA has become increasingly popular in recent years with several excellent books (e. g. Cichocki and Amari 2002; Haykin 2000; Hyvärinen et al. 2001) and a large number of papers being published. Its attractiveness is explained by its relative simplicity as well as from a large number of application areas (Haykin 2000). For example, ICA has been successfully employed in sound separation (Lee 1998), financial forecasting (Back and Weingend 1997), biomedical data processing (Lee 1998), image filtering (Cichocki and Amari 2002), and remote sensing (Tu et al. 2001). While in many of these applications, the problems do not exactly fit the required setting, ICA based algorithms have been shown to be robust enough to produce accurate solutions. In fact, it is this robustness that has fueled the theoretical advances in this area (Haykin 2000).