Work

An EM Stopping Rule for Avoiding Degeneracy in Gaussian-Based Clustering withMissing Data.

Year: 2025

Type: conference-paper

Abstract: Missing data frequency increases with the growing size of multivariate moderndatasets. In Gaussian model-based clustering, the EM algorithm easily takes into account suchdata but the degeneracy problem is dramatically aggravated during the EM runs: parameter de-generacy is quite slow and also more frequent than with complete data. Consequently, param-eter degenerated solutions may be confused with valuable parameter solutions and, in addition,computing time may be wasted through wrong runs. In this work, a simple and low informational condition on the latent partition allows to propose a very simple partition-based stoppingrule of EM which shows good behavior on nu- merical experiments.

Source: HAL (Le Centre pour la Communication Scientifique Directe)

Authors: Biernacki, Christophe, Vandewalle, Vincent

Institutions: Université de Lille, Laboratoire Paul Painlevé, Centre National de la Recherche Scientifique, Université Côte d'Azur, Laboratoire Jean-Alexandre Dieudonné, MAASAI: Modèles et algorithmes pour l'intelligence artificielle

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7116211998

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