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Maximum Likelihood for Gaussians on Graphs

  • Conference paper
Graph-Based Representations in Pattern Recognition (GbRPR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6658))

Abstract

We show that extending the Gaussian distribution to the domain of graphs corresponds to truncated Gaussian distributions in Euclidean spaces. Based on this observation, we derive a maximum likelihood method for estimating the parameters of the Gaussian on graphs. In conjunction with a naive Bayes classifier, we applied the proposed approach to image classification.

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Author information

Authors and Affiliations

  1. Berlin Institute of Technology, Germany

    Brijnesh J. Jain & Klaus Obermayer

Authors
  1. Brijnesh J. Jain
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  2. Klaus Obermayer
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Einsteinstraße 62, 48149, Münster, Germany

    Xiaoyi Jiang

  2. Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya, C. Llorens i Artigas 4-6, 08028, Barcelona, Spain

    Miquel Ferrer

  3. Dipartimento di Scienze Ambientali, Informatica, Statistica, Università Ca’ Foscari di Venezia, Via Torino 155, 30172, Venezia Mestre, Italy

    Andrea Torsello

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© 2011 Springer-Verlag Berlin Heidelberg

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Jain, B.J., Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs. In: Jiang, X., Ferrer, M., Torsello, A. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2011. Lecture Notes in Computer Science, vol 6658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20844-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-20844-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20843-0

  • Online ISBN: 978-3-642-20844-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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