Clustering of Signals Using Incomplete Independent Component Analysis

In this paper we propose a new algorithm for the clustering of signals using incomplete independent component analysis (ICA). In the first step we apply the ICA to the dataset without dimension reduction, in the second step we reduce the dimension of the data to find clusters of independent components that are similar in their entries in the mixture matrix found by the ICA. We demonstrate that our algorithm out-performs k-means in the case of toy data and works well with a real world fMRI example, thus allowing a closer look the way how different parts of the brain work together.