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Communication Dans Un Congrès Année : 2024

A unified framework for hard and soft clustering with regularized optimal transport

Jean-Frédéric Diebold
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  • PersonId : 1037669
Arnaud Dessein
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  • PersonId : 885068

Résumé

In this short paper, we formulate parameter estimation for finite mixture models in the context of discrete optimal transportation with convex regularization. The proposed framework unifies hard and soft clustering methods for general mixture models. It also generalizes the celebrated $k$\nobreakdash-means and expectation-maximization algorithms in relation to associated Bregman divergences when applied to exponential family mixture models.

Dates et versions

hal-01635325 , version 1 (15-11-2017)

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Jean-Frédéric Diebold, Nicolas Papadakis, Arnaud Dessein, Charles-Alban Deledalle. A unified framework for hard and soft clustering with regularized optimal transport. European conference on signal processing, Aug 2024, Lyon, France. ⟨hal-01635325⟩
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