Estimation of Mixture Models Using Co-EM

  • Steffen Bickel
  • Tobias Scheffer
Conference paper

DOI: 10.1007/11564096_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)
Cite this paper as:
Bickel S., Scheffer T. (2005) Estimation of Mixture Models Using Co-EM. In: Gama J., Camacho R., Brazdil P.B., Jorge A.M., Torgo L. (eds) Machine Learning: ECML 2005. ECML 2005. Lecture Notes in Computer Science, vol 3720. Springer, Berlin, Heidelberg

Abstract

We study estimation of mixture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or research papers that have intrinsic (text) and extrinsic (references) attributes. Our optimization criterion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. We derive an algorithm that maximizes this criterion. Empirically, we observe that the resulting clustering method incurs a lower cluster entropy than regular EM for web pages, research papers, and many text collections.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Steffen Bickel
    • 1
  • Tobias Scheffer
    • 1
  1. 1.School of Computer ScienceHumboldt-Universität zu BerlinBerlinGermany

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