Generalized topographic block model - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Neurocomputing Année : 2016

Generalized topographic block model

Résumé

Co-clustering leads to parsimony in data visualisation with a number of parameters dramatically reduced in comparison to the dimensions of the data sample. Herein, we propose a new generalized approach for nonlinear mapping by a re-parameterization of the latent block mixture model. The densities modeling the blocks are in an exponential family such that the Gaussian, Bernoulli and Poisson laws are particular cases. The inference of the parameters is derived from the block expectation-maximization algorithm with a Newton-Raphson procedure at the maximization step. Empirical experiments with textual data validate the interest of our generalized model.
Fichier principal
Vignette du fichier
__userfiles.soton.ac.uk_Library_SLAs_Work_for_ALL%2527s_Work_for_ePrints_Accepted%2520Manuscripts_Priam_Generalized.pdf (2.9 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01285593 , version 1 (30-01-2024)

Identifiants

Citer

Rodolphe Priam, Mohamed Nadif, Gérard Govaert. Generalized topographic block model. Neurocomputing, 2016, 173 (supl Part2), pp.442-449. ⟨10.1016/j.neucom.2015.04.115⟩. ⟨hal-01285593⟩
169 Consultations
12 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More