Clustering of signals using incomplete independent component analysis

Authors:
Ingo R. Keck;Elmar W. Lang;Salua Nassabay;Carlos G. Puntonet
Affiliations:
Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, Regensburg, Germany;Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, Regensburg, Germany;Departamento de Arquitectura y Tecnologia de Computadores, Universidad de Granada/ESII, Granada, Spain;Departamento de Arquitectura y Tecnologia de Computadores, Universidad de Granada/ESII, Granada, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

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.