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Article Dans Une Revue Pattern Recognition Année : 2010

Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach

Résumé

Aggregating statistical representations of classes is an important task for current trends in scaling up learning and recognition, or for addressing them in distributed in- frastructures. In this perspective, we address the problem of merging probabilistic Gaus- sian mixture models in an efficient way, through the search for a suitable combination of components from mixtures to be merged. We propose a new Bayesian modelling of this combination problem, in association to a variational estimation technique, that handles efficiently the model complexity issue. A main feature of the present scheme is that it merely resorts to the parameters of the original mixture, ensuring low computational cost and possibly communication, should we operate on a distributed system. Experimental results are reported on real data
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Dates et versions

hal-00414325 , version 1 (08-09-2009)

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Pierrick Bruneau, Marc Gelgon, Fabien Picarougne. Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach. Pattern Recognition, 2010, 43, pp.850-858. ⟨10.1016/j.patcog.2009.08.006⟩. ⟨hal-00414325⟩
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