Charles Bouveyron

2.9k total citations · 1 hit paper
70 papers, 1.4k citations indexed

About

Charles Bouveyron is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Charles Bouveyron has authored 70 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 11 papers in Signal Processing. Recurrent topics in Charles Bouveyron's work include Bayesian Methods and Mixture Models (28 papers), Advanced Clustering Algorithms Research (20 papers) and Face and Expression Recognition (8 papers). Charles Bouveyron is often cited by papers focused on Bayesian Methods and Mixture Models (28 papers), Advanced Clustering Algorithms Research (20 papers) and Face and Expression Recognition (8 papers). Charles Bouveyron collaborates with scholars based in France, Italy and Ireland. Charles Bouveyron's co-authors include Stéphane Girard, Julien Jacques, C. Schmid, Étienne Côme, Pierre Latouche, Cordelia Schmid, Laurent Bergé, Julie Delon, Laurence Chèze and Juan Sebastián Ulloa and has published in prestigious journals such as Scientific Reports, Pattern Recognition and Astronomy and Astrophysics.

In The Last Decade

Charles Bouveyron

62 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Model-based clustering of high-dimensional data: A review

2012 281 citations Charles Bouveyron et al. profile →

Peers

Charles Bouveyron

AI

SP

CVPR

MB

Jeffrey D. Banfield United States

Gérard Govaert France

Yves Grandvalet France

Nickolay T. Trendafilov United Kingdom

David Luengo Spain

Rahul Mazumder United States

Craig Saunders United Kingdom

Andreas Weingessel Austria

Cyril Goutte Canada

Junhui Wang United States

Jeffrey D. Banfield United States

Charles Bouveyron

950 ×1.3

AI

229 ×1.0

SP

352 ×1.5

SP

249 ×1.2

CVPR

195 ×1.1

MB

Citations per year, relative to Charles Bouveyron Charles Bouveyron (= 1×) peers Jeffrey D. Banfield

Countries citing papers authored by Charles Bouveyron

Since Specialization

Citations

This map shows the geographic impact of Charles Bouveyron's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Charles Bouveyron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Bouveyron more than expected).

Fields of papers citing papers by Charles Bouveyron

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles Bouveyron. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charles Bouveyron. The network helps show where Charles Bouveyron may publish in the future.

Co-authorship network of co-authors of Charles Bouveyron

This figure shows the co-authorship network connecting the top 25 collaborators of Charles Bouveyron. A scholar is included among the top collaborators of Charles Bouveyron based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charles Bouveyron. Charles Bouveyron is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Destère, Alexandre, et al.. (2024). An artificial intelligence algorithm for co‐clustering to help in pharmacovigilance before and during the COVID‐19 pandemic. British Journal of Clinical Pharmacology. 90(5). 1258–1267. 2 indexed citations

2.

Destère, Alexandre, et al.. (2024). Deep dynamic co-clustering of count data streams: application to pharmacovigilance. SPIRE - Sciences Po Institutional REpository. 4(4). 1 indexed citations

3.

Bouveyron, Charles, et al.. (2024). A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices. Journal of Computational and Graphical Statistics. 33(4). 1224–1239. 1 indexed citations

4.

Bouveyron, Charles, et al.. (2024). Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea. Ecological Informatics. 85. 102959–102959. 1 indexed citations

5.

Bouveyron, Charles, et al.. (2023). The graph embedded topic model. Neurocomputing. 562. 126900–126900. 3 indexed citations

6.

Bouveyron, Charles, et al.. (2023). Embedded topics in the stochastic block model. Statistics and Computing. 33(5).

7.

Bouveyron, Charles, Damien Piga, Stéphane Descombes, et al.. (2022). Cardiac dyspnea risk zones in the South of France identified by geo-pollution trends study. Scientific Reports. 12(1). 1900–1900. 1 indexed citations

8.

Fop, Michael, et al.. (2021). Unobserved classes and extra variables in high-dimensional discriminant\n analysis. arXiv (Cornell University). 1 indexed citations

9.

Bouveyron, Charles, et al.. (2021). Co-clustering of Time-Dependent Data via the Shape Invariant Model. HAL (Le Centre pour la Communication Scientifique Directe). 8 indexed citations

10.

Fraix‐Burnet, Didier, Charles Bouveyron, & J. Moultaka. (2021). Unsupervised classification of SDSS galaxy spectra. Astronomy and Astrophysics. 649. A53–A53. 10 indexed citations

11.

Bouveyron, Charles, et al.. (2020). A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace\n clustering. arXiv (Cornell University). 1 indexed citations

12.

Cesaretti, Manuela, Jocelyn Gal, Charles Bouveyron, et al.. (2020). Accurate assessment of nonalcoholic fatty liver disease lesions in liver allograft biopsies by a smartphone platform: A proof of concept. Microscopy Research and Technique. 83(9). 1025–1031. 6 indexed citations

13.

Delon, Julie, et al.. (2019). A Unified View on Patch Aggregation. Journal of Mathematical Imaging and Vision. 62(2). 149–168. 2 indexed citations

14.

Bouveyron, Charles, et al.. (2016). Globally Sparse Probabilistic PCA. International Conference on Artificial Intelligence and Statistics. 976–984. 4 indexed citations

15.

Bouveyron, Charles, et al.. (2016). The stochastic topic block model for the clustering of vertices in networks with textual edges. Statistics and Computing. 28(1). 11–31. 15 indexed citations

16.

Bouveyron, Charles. (2014). Statistique en grande dimension : problématiques et enjeux. French digital mathematics library (Numdam). 155(2). 36–37. 1 indexed citations

17.

Bouveyron, Charles, Barbara Hammer, & Thomas Villmann. (2012). Recent developments in clustering algorithms. The European Symposium on Artificial Neural Networks. 10(4). 10 indexed citations

18.

Bouveyron, Charles, et al.. (2009). Classification automatique dans les sous-espaces discriminants de Fisher. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

19.

Bouveyron, Charles, et al.. (2009). Classification of high dimensional data for cervical cancer detection. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

20.

Bouveyron, Charles, Stéphane Girard, & Cordelia Schmid. (2007). High-Dimensional Discriminant Analysis. Communication in Statistics- Theory and Methods. 36(14). 2607–2623. 56 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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