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

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierReprésentations musicales [Repmus]
dc.contributor.authorDESSEIN, Arnaud
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDELEDALLE, Charles-Alban
dc.date.accessioned2024-04-04T03:07:59Z
dc.date.available2024-04-04T03:07:59Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193492
dc.description.abstractEnIn 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.
dc.description.sponsorshipGeneralized Optimal Transport Models for Image processing - ANR-16-CE33-0010
dc.language.isoen
dc.title.enParameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halStatistiques [stat]/Autres [stat.ML]
dc.identifier.arxiv1711.04366
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-01635325
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01635325v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=PAPADAKIS,%20Nicolas&DESSEIN,%20Arnaud&DELEDALLE,%20Charles-Alban&rft.genre=preprint


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