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Second-Order Source Separation Based on Prior Knowledge Realized in a Graph Model

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Latent Variable Analysis and Signal Separation (LVA/ICA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6365))

Abstract

Matrix factorization techniques provide efficient tools for the detailed analysis of large-scale biological and biomedical data. While underlying algorithms usually work fully blindly, we propose to incorporate prior knowledge encoded in a graph model. This graph introduces a partial ordering in data without intrinsic (e.g. temporal or spatial) structure, which allows the definition of a graph-autocorrelation function. Using this framework as constraint to the matrix factorization task we develop a second-order source separation algorithm called graph-decorrelation algorithm (GraDe). We demonstrate its applicability and robustness by analyzing microarray data from a stem cell differentiation experiment.

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Author information

Authors and Affiliations

  1. Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764, Neuherberg, Germany

    Florian Blöchl, Andreas Kowarsch & Fabian J. Theis

  2. Institute for Mathematical Sciences, TU München, 85747, Garching, Germany

    Fabian J. Theis

  3. Equal contributors,  

    Florian Blöchl & Andreas Kowarsch

Authors
  1. Florian Blöchl
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  2. Andreas Kowarsch
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  3. Fabian J. Theis
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Editor information

Editors and Affiliations

  1. Dept. of Electrical Engineering, Universitè d’Evry Val d’Essone, 40 rue du Pelvoux, 91020, Courcouronnes, France

    Vincent Vigneron

  2. Laboratoire I3S, Les Algorithmes - Euclide-B, BP 121, Université de Nice-Sophia Antipolis, 2000 Route des Lucioles, 06903, Sophia Antipolis Cedex, France

    Vicente Zarzoso

  3. School of Engineering, Dept. of Telecommunications, ISITSchool of Engineering, Dept. of Telecommunications, ISITV, Université de Toulon, Avenue George Pompidou, BP 56, La Valette du Var, Cedex, 83162, France

    Eric Moreau

  4. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes cedex, France

    Rémi Gribonval

  5. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France

    Emmanuel Vincent

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© 2010 Springer-Verlag Berlin Heidelberg

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Blöchl, F., Kowarsch, A., Theis, F.J. (2010). Second-Order Source Separation Based on Prior Knowledge Realized in a Graph Model. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_54

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  • DOI: https://doi.org/10.1007/978-3-642-15995-4_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15994-7

  • Online ISBN: 978-3-642-15995-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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