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Extracting Coactivated Features from Multiple Data Sets

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Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6791))

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Abstract

We present a nonlinear generalization of Canonical Correlation Analysis (CCA) to find related structure in multiple data sets. The new method allows to analyze an arbitrary number of data sets, and the extracted features capture higher-order statistical dependencies. The features are independent components that are coupled across the data sets. The coupling takes the form of coactivation (dependencies of variances). We validate the new method on artificial data, and apply it to natural images and brain imaging data.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and HIIT, University of Helsinki, P.O. Box 68, FIN-00014, Finland

    Michael U. Gutmann & Aapo Hyvärinen

  2. Dept. of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FIN-00014, Finland

    Michael U. Gutmann & Aapo Hyvärinen

Authors
  1. Michael U. Gutmann
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  2. Aapo Hyvärinen
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Editor information

Editors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Timo Honkela  & Samuel Kaski  & 

  2. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK

    Mark Girolami

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© 2011 Springer-Verlag Berlin Heidelberg

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Gutmann, M.U., Hyvärinen, A. (2011). Extracting Coactivated Features from Multiple Data Sets. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_40

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  • DOI: https://doi.org/10.1007/978-3-642-21735-7_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21734-0

  • Online ISBN: 978-3-642-21735-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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