Model-based co-clustering for ordinal data
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Model-based co-clustering for ordinal data
Author(s) :
Jacques, Julien [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Entrepôts, Représentation et Ingénierie des Connaissances [ERIC]
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Entrepôts, Représentation et Ingénierie des Connaissances [ERIC]
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Conference title :
48èmes Journées de Statistique organisée par la Société Française de Statistique
City :
Montpellier
Country :
France
Start date of the conference :
2016
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Statistiques [stat]/Théorie [stat.TH]
English abstract : [en]
A model-based coclustering algorithm for ordinal data is presented. Thisalgorithm relies on the latent block model using the BOS model (Biernacki and Jacques,2015, Stat. Comput.) for ordinal data and a SEM-Gibbs algorithm ...
Show more >A model-based coclustering algorithm for ordinal data is presented. Thisalgorithm relies on the latent block model using the BOS model (Biernacki and Jacques,2015, Stat. Comput.) for ordinal data and a SEM-Gibbs algorithm for inference. Nu-merical experiments on simulated data illustrate the eciency of the inference strategy.Show less >
Show more >A model-based coclustering algorithm for ordinal data is presented. Thisalgorithm relies on the latent block model using the BOS model (Biernacki and Jacques,2015, Stat. Comput.) for ordinal data and a SEM-Gibbs algorithm for inference. Nu-merical experiments on simulated data illustrate the eciency of the inference strategy.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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