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D-Index & Metrics

Engineering and Technology

D-Index
46
Citations
17980
World Ranking
5024
National Ranking
100

Overview

Gilles Celeux is affiliated with the French Institute for Research in Computer Science and Automation (INRIA) in France. Their research primarily spans the field of computer science, with a focus on artificial intelligence, statistics and probability, signal processing, media technology, and analytical chemistry.

Celeux's recent publications reflect a concentration on advanced clustering methods, statistical modeling, and data management. Notable works include:

  • "Hierarchical Clustering of Spectral Images with Spatial Constraints for the Rapid Processing of Large and Heterogeneous Data Sets," 2022, SN Computer Science
  • "Model-based clustering with missing not at random data," 2024, Statistics and Computing
  • "Combining Methods in Supervised Classification: a Comparative Study on Discrete and Continuous Problems," 2022, DOAJ (DOAJ: Directory of Open Access Journals)
  • "Model-based Clustering with Missing Not At Random Data," 2021, arXiv (Cornell University)
  • "Estimating Parameters of the Weibull Competing Risk Model with Masked Causes and Heavily Censored Data," 2021, Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

The main topics addressed in their work include:

  • Bayesian Methods and Mixture Models
  • Advanced Clustering Algorithms Research
  • Data Management and Algorithms
  • Statistical and Computational Modeling
  • Neural Networks and Applications
  • Statistical Methods and Inference
  • Geochemistry and Geologic Mapping

Frequent co-authors collaborating with Celeux are:

  • Aude Sportisse
  • Matthieu Marbac
  • Fabien Laporte
  • Claire Boyer
  • Christophe Biernacki

Celeux's research contributions have been published in several venues, with multiple papers appearing in "Statistique et société," as well as journals such as "Statistics and Computing," "DOAJ," "SN Computer Science," and "arXiv (Cornell University)." These platforms underline the interdisciplinary and methodological aspects of their research in statistics and computer science.

Best Publications

  • An entropy criterion for assessing the number of clusters in a mixture model

    Gilles Celeux;Gilda Soromenho

  • Assessing a mixture model for clustering with the integrated completed likelihood

    C. Biernacki;G. Celeux;G. Govaert

  • Gaussian parsimonious clustering models

    Gilles Celeux;Gérard Govaert

  • A Classification EM algorithm for clustering and two stochastic versions

    Gilles Celeux;Gérard Govaert

  • Deviance information criteria for missing data models

    G. Celeux;F. Forbes;C. P. Robert;D. M. Titterington

  • Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models

    Christophe Biernacki;Gilles Celeux;Gérard Govaert

  • Computational and inferential difficulties with mixture posterior distributions

    Gilles Celeux;Merrilee Hurn;Christian P. Robert

  • Introduction to the special section on video surveillance

    R.T. Collins;A.J. Lipton;T. Kanade

  • EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Image Segmentation

    Gilles Celeux;Florence Forbes;Nathalie Peyrard

  • Combining Mixture Components for Clustering

    Jean Patrick Baudry;Adrian E. Raftery;Gilles Celeux;Kenneth Lo

  • On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply

    S. Richardson;P. J. Green;C. P. Robert;M. Aitkin

  • Variable selection for clustering with Gaussian mixture models.

    Cathy Maugis;Gilles Celeux;Marie-Laure Martin-Magniette;Marie-Laure Martin-Magniette

  • Inference in model-based cluster analysis

    Halima Bensmail;Gilles Celeux;Adrian E. Raftery;Christian P. Robert

  • Data-based filtering for replicated high-throughput transcriptome sequencing experiments

    Andrea Rau;Mélina Gallopin;Gilles Celeux;Florence Jaffrézic

  • Stochastic versions of the em algorithm: an experimental study in the mixture case

    Gilles Celeux;Didier Chauveau;Jean Diebolt

  • Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition

    Halima Bensmail;Gilles Celeux

  • Bayesian estimation of hidden Markov chains: a stochastic implementation

    Christian P. Robert;Gilles Celeux;Jean Diebolt

  • Model-based cluster and discriminant analysis with the MIXMOD software

    Christophe Biernacki;Gilles Celeux;Gérard Govaert;Florent Langrognet

  • An improvement of the NEC criterion for assessing the number of clusters in a mixture model

    Christophe Biernacki;Gilles Celeux;Gérand Govaert

  • Selecting hidden Markov model state number with cross-validated likelihood

    Gilles Celeux;Jean-Baptiste Durand

Frequent Co-Authors

Christian P. Robert
Christian P. Robert Paris Dauphine University
Jean-Michel Marin
Jean-Michel Marin University of Montpellier
Adrian E. Raftery
Adrian E. Raftery University of Washington
D. M. Titterington
D. M. Titterington University of Glasgow
Noel A Cressie
Noel A Cressie University of Wollongong
Walter R. Gilks
Walter R. Gilks University of Leeds
Jorge S. Marques
Jorge S. Marques Instituto Superior Técnico
Geoffrey J. McLachlan
Geoffrey J. McLachlan University of Queensland
Raphael Gottardo
Raphael Gottardo Fred Hutchinson Cancer Research Center
Jean-Pierre Renou
Jean-Pierre Renou University of Angers

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