Injecting noise for analysing the stability of ICA components
Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliabilityand the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well byour method. In a toyexperiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratorydata analysis of real-world data.