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Communication Dans Un Congrès Année : 2009

Parsimonious variational-Bayes mixture aggregation with a Poisson prior

Résumé

This paper addresses merging of Gaussian mixture models, which answers growing needs in e.g. distributed pattern recognition. We propose a probabilistic model over the parameter set, that extends the weighted bipartite matching problem to our mixture aggregation task. We then derive a variational- Bayes associated estimation algorithm, that ensure low cost and parsimony, as confirmed by experimental results.
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Dates et versions

inria-00383945 , version 1 (13-05-2009)

Identifiants

  • HAL Id : inria-00383945 , version 1

Citer

Pierrick Bruneau, Marc Gelgon, Fabien Picarougne. Parsimonious variational-Bayes mixture aggregation with a Poisson prior. European Signal Processing Conference (Eusipco'2009), Aug 2009, Glasgow, United Kingdom. pp.280-284. ⟨inria-00383945⟩
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