Reliability of ICA estimates with mutual information

Abstract

Obtaining the most independent components from a mixture (under a chosen model) is only the first part of an ICA analysis. After that, it is necessary to measure the actual dependency between the components and the reliability of the decomposition. We have to identify one- and multidimensional components (i.e., clusters of mutually dependent components) or channels which are too close to Gaussians to be reliably separated. For the determination of the dependencies we use a new highly accurate mutual information (MI) estimator. The variability of the MI under remixing provides us a measure for the stability. A rapid growth of the MI under mixing identifies stable components. On the other hand a low variability identifies unreliable components. The method is illustrated on artificial datasets. The usefulness in real-world data is shown on biomedical data. © Springer-Verlag 2004.