The Latent Block Model: a useful model for high dimensional data - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

The Latent Block Model: a useful model for high dimensional data

Résumé

The Latent Block Model (LBM) designs in a same exercise a clustering of the rows and the columns of a data array. Typically the LBM is expected to be useful to analyze huge data sets with many observations and many variables. But it encounters several numerical issues with big data set: maximum likelihood is jeopardized by spurious maxima and selecting a proper model is challenging since there are a lot of models are in competition. In this communication, we analyze these numerical issues. In particular, we make use of Bayesian inference to avoid spurious solutions and propose an efficient way to scan the model set. Moreover, we advocate the exact Integrated Completed Likelihood (ICL) criterion to select a proper and consistent LBM. The methods and algorithms will be ilustrated with pharmacovigilance data involving large arrays of data.
Fichier principal
Vignette du fichier
KERIBIN-CELEUX-ROBERT-ISI17.pdf (426.86 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01658589 , version 1 (07-12-2017)

Identifiants

  • HAL Id : hal-01658589 , version 1

Citer

Christine Keribin, Gilles Celeux, Valérie Robert. The Latent Block Model: a useful model for high dimensional data. ISI 2017 - 61st world statistics congress, Jul 2017, Marrakech, Morocco. pp.1-6. ⟨hal-01658589⟩
791 Consultations
1144 Téléchargements

Partager

Gmail Facebook X LinkedIn More