Structure Learning with Nonparametric Decomposable Models

  • Anton Schwaighofer
  • Mathäus Dejori
  • Volker Tresp
  • Martin Stetter
Conference paper

DOI: 10.1007/978-3-540-74690-4_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)
Cite this paper as:
Schwaighofer A., Dejori M., Tresp V., Stetter M. (2007) Structure Learning with Nonparametric Decomposable Models. In: de Sá J.M., Alexandre L.A., Duch W., Mandic D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg

Abstract

We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete and continuous distributions can be handled in a unified framework. Also, consistency of the underlying probabilistic model is guaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Anton Schwaighofer
    • 1
  • Mathäus Dejori
    • 2
  • Volker Tresp
    • 2
  • Martin Stetter
    • 2
  1. 1.Fraunhofer FIRST, Intelligent Data Analysis Group, Kekulestr. 7, 12489 BerlinGermany
  2. 2.Siemens Corporate Technology, 81730 MunichGermany

Personalised recommendations