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Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-01635325
Contributor : Nicolas Papadakis <>
Submitted on : Wednesday, November 15, 2017 - 9:09:45 AM
Last modification on : Monday, July 22, 2019 - 11:00:09 AM

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  • HAL Id : hal-01635325, version 1
  • ARXIV : 1711.04366

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Nicolas Papadakis, Arnaud Dessein, Charles-Alban Deledalle. Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering. 2017. ⟨hal-01635325⟩

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