@inproceedings{2009-C-GMMQuantization-EUSIPCO
, author={Frank Nielsen and Vincent Garcia and Richard Nock}
, title={Gaussian mixture models via entropic quantization}
, booktitle={2009 European Signal Processing Conference (EUSIPCO)}
, month={August}
, year={2009}
, pages={2012-2016}
, address={Glasgow, United Kingdom}
, publisher={IEEE}
, abstract={
  Mixture models are a crucial statistical modeling tool at the heart of many challenging
 applications in computer vision, machine learning, and text classification among
 others. In this paper, we describe a novel and efficient algorithm for simplifying
 Gaussian mixture models using a generalization of the celebrated k-means quantization
 algorithm tailored to relative entropy in statistical distribution spaces. Our algorithm
 extends easily to arbitrary mixture of exponential families. The proposed method
 is shown to compare favourably well with the state-of-the-art unscented transform
 clustering algorithm both in terms of time and quality performances.
  }
}