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Clustering of Signals Using Incomplete Independent Component Analysis

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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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.

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Author information

Authors and Affiliations

  1. Institute of Biophysics, Neuro- and Bioinformatics Group, University of Regensburg, D-93040, Regensburg, Germany

    Ingo R. Keck & Elmar W. Lang

  2. Departamento de Arquitectura y Tecnologia de Computadores, Universidad de Granada/ESII, E-1807, Granada, Spain

    Salua Nassabay & Carlos G. Puntonet

Authors
  1. Ingo R. Keck
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  2. Elmar W. Lang
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  3. Salua Nassabay
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  4. Carlos G. Puntonet
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Keck, I.R., Lang, E.W., Nassabay, S., Puntonet, C.G. (2005). Clustering of Signals Using Incomplete Independent Component Analysis. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_131

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  • DOI: https://doi.org/10.1007/11494669_131

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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