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Comix: Joint Estimation and Lightspeed Comparison of Mixture Models

Authors
  • Schwander, Olivier
  • Marchand-Maillet, Stéphane
  • Nielsen, Frank
Publication Date
Jan 01, 2016
Source
HAL-UPMC
Keywords
Language
English
License
Unknown
External links

Abstract

The Kullback-Leibler divergence is a widespread dis-similarity measure between probability density functions , based on the Shannon entropy. Unfortunately, there is no analytic formula available to compute this divergence between mixture models, imposing the use of costly approximation algorithms. In order to reduce the computational burden when a lot of divergence evaluations are needed, we introduce a sub-class of the mixture models where the component parameters are shared between a set of mixtures and the only degree-of-freedom is the vector of weights of each mixture. This sharing allows to design extremely fast versions of existing dis-similarity measures between mixtures. We demonstrate the effectiveness of our approach by evaluating the quality of the ordering produced by our method on a real dataset.

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