A variational Expectation–Maximization algorithm for temporal data clustering

The problem of temporal data clustering is addressed using a dynamic Gaussian mixture model. In addition to the missing clusters used in the classical Gaussian mixture model, the proposed approach assumes that the means of the Gaussian densities are latent variables distributed according to random w...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 103; pp. 206 - 228
Main Authors:	El Assaad, Hani, Samé, Allou, Govaert, Gérard, Aknin, Patrice
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.11.2016 Elsevier
Subjects:	Algorithms Approximation Clustering Data Analysis, Statistics and Probability Dynamic latent variable model EM algorithm Estimates Gaussian Kalman filter Mathematical models Maximum likelihood Mixture model Parameter estimation Parameters Physics statistical analysis Temporal data clustering Variational approximation Kalman filter Dynamic latent variable model Mixture model Variational approximation Maximum likelihood Clustering EM algorithm Temporal data clustering DYNAMIC LATENT VARIABLE MODEL TEMPORAL DATA CLUSTERING CLUSTERING MODELE DE MELANGE ALGORITHME ESPERANCE-MAXIMISATION FILTRE DE KALMAN VARIATIONAL APPROXIMATION
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
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