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.

[1]  Michael I. Jordan,et al.  Beyond Independent Components: Trees and Clusters , 2003, J. Mach. Learn. Res..

[2]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[3]  Aapo Hyvärinen,et al.  Topographic Independent Component Analysis , 2001, Neural Computation.

[4]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[5]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[6]  Shun-ichi Amari,et al.  Natural Gradient Learning for Over- and Under-Complete Bases in ICA , 1999, Neural Computation.

[7]  Karl J. Friston,et al.  Human Brain Function , 1997 .

[8]  Anke Meyer-Bäse,et al.  Tree-Dependent and Topographic Independent Component Analysis for fMRI Analysis , 2004, ICA.

[9]  E. Oja,et al.  Independent Component Analysis , 2013 .

[10]  Fabian J. Theis,et al.  Linear Geometric ICA: Fundamentals and Algorithms , 2003, Neural Computation.

[11]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[12]  Allan Kardec Barros,et al.  Independent Component Analysis and Blind Source Separation , 2007, Signal Processing.

[13]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[14]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..