HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach

Abstract : This paper proposes a technique for simplifying a given Gaussian mixture model, i.e. reformulating the density in a more parcimonious manner, if possible (less Gaussian components in the mixture). Numerous applications requiring aggregation of models from various sources, or index structures over sets of mixture models for fast access, may benefit from the technique. Variational Bayesian estimation of mixtures is known to be a powerful technique on punctual data. We derive herein a new version of the Variational-Bayes EM algorithm that operates on Gaussian components of a given mixture and suppresses redundancy, if any, while preserving structure of the underlying generative process. A main feature of the present scheme is that it merely resorts to the parameters of the original mixture, ensuring low computational cost. Experimental results are reported on real data.
Document type :
Conference papers
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download

Contributor : Marc Gelgon Connect in order to contact the contributor
Submitted on : Tuesday, March 17, 2009 - 7:46:13 PM
Last modification on : Wednesday, April 27, 2022 - 4:11:41 AM
Long-term archiving on: : Friday, October 12, 2012 - 1:45:39 PM


Publisher files allowed on an open archive


  • HAL Id : inria-00368883, version 1


Pierrick Bruneau, Marc Gelgon, Fabien Picarougne. Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach. International Conference on Pattern Recognition (ICPR'2008), 2008, Tampa, United States. pp.450-453. ⟨inria-00368883⟩



Record views


Files downloads