Combinatorial Markov Random Fields

  • Ron Bekkerman
  • Mehran Sahami
  • Erik Learned-Miller
Conference paper

DOI: 10.1007/11871842_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)
Cite this paper as:
Bekkerman R., Sahami M., Learned-Miller E. (2006) Combinatorial Markov Random Fields. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science, vol 4212. Springer, Berlin, Heidelberg

Abstract

A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comrafs in perspective by showing their relationship with several existing models. Since it can be problematic to apply existing inference techniques for graphical models to Comrafs, we design two simple and efficient inference algorithms specific for Comrafs, which are based on combinatorial optimization. We show that even such simple algorithms consistently and significantly outperform Latent Dirichlet Allocation (LDA) on a document clustering task. We then present Comraf models for semi-supervised clustering and transfer learning that demonstrate superior results in comparison to an existing semi-supervised scheme (constrained optimization).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ron Bekkerman
    • 1
  • Mehran Sahami
    • 2
  • Erik Learned-Miller
    • 1
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherstUSA
  2. 2.Google Inc.Mountain ViewUSA

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