Michaël Aupetit

1.7k total citations
54 papers, 999 citations indexed

About

Michaël Aupetit is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Michaël Aupetit has authored 54 papers receiving a total of 999 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 9 papers in Signal Processing. Recurrent topics in Michaël Aupetit's work include Data Visualization and Analytics (14 papers), Advanced Clustering Algorithms Research (9 papers) and Image Retrieval and Classification Techniques (8 papers). Michaël Aupetit is often cited by papers focused on Data Visualization and Analytics (14 papers), Advanced Clustering Algorithms Research (9 papers) and Image Retrieval and Classification Techniques (8 papers). Michaël Aupetit collaborates with scholars based in Qatar, France and United States. Michaël Aupetit's co-authors include Luís Gustavo Nonato, Michael Sedlmair, Sylvain Lespinats, Luis Fernández-Luque, Shahrad Taheri, João Palotti, Raghvendra Mall, Juan M. García‐Gómez, Yu Guan and Ignacio Perez-Pozuelo and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Access.

In The Last Decade

Michaël Aupetit

49 papers receiving 950 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michaël Aupetit Qatar 15 528 385 149 136 107 54 999
Carlos Eduardo Thomaz Brazil 18 529 1.0× 161 0.4× 337 2.3× 80 0.6× 152 1.4× 103 1.2k
Youngjune Gwon United States 13 381 0.7× 304 0.8× 59 0.4× 184 1.4× 170 1.6× 45 1.2k
Bernardino Romera‐Paredes United Kingdom 14 241 0.5× 326 0.8× 87 0.6× 192 1.4× 36 0.3× 18 1.0k
Yannis Panagakis United Kingdom 21 854 1.6× 332 0.9× 133 0.9× 203 1.5× 533 5.0× 89 1.5k
Zhuhong Shao China 18 780 1.5× 164 0.4× 95 0.6× 270 2.0× 90 0.8× 61 1.1k
M. Fatih Demirci Türkiye 14 516 1.0× 218 0.6× 51 0.3× 312 2.3× 326 3.0× 55 957
J.F. Tasič Slovenia 15 275 0.5× 165 0.4× 98 0.7× 39 0.3× 64 0.6× 97 1.1k
L. Zhao United States 15 577 1.1× 367 1.0× 27 0.2× 29 0.2× 57 0.5× 41 996
Aravind Ganapathiraju United States 12 279 0.5× 654 1.7× 98 0.7× 111 0.8× 500 4.7× 35 1.2k
Gianluigi Ciocca Italy 22 1.4k 2.6× 184 0.5× 66 0.4× 24 0.2× 141 1.3× 90 1.9k

Countries citing papers authored by Michaël Aupetit

Since Specialization
Citations

This map shows the geographic impact of Michaël Aupetit'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 Michaël Aupetit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michaël Aupetit more than expected).

Fields of papers citing papers by Michaël Aupetit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michaël Aupetit. 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 Michaël Aupetit. The network helps show where Michaël Aupetit may publish in the future.

Co-authorship network of co-authors of Michaël Aupetit

This figure shows the co-authorship network connecting the top 25 collaborators of Michaël Aupetit. A scholar is included among the top collaborators of Michaël Aupetit 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 Michaël Aupetit. Michaël Aupetit is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jeon, Hyeon, Michaël Aupetit, Soohyun Lee, et al.. (2025). Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections. IEEE Transactions on Visualization and Computer Graphics. 32(2). 2165–2182.
2.
Jeon, Hyeon, et al.. (2025). Measuring the Validity of Clustering Validation Datasets. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(6). 5045–5058. 2 indexed citations
3.
Aupetit, Michaël, et al.. (2024). Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review. Journal of Medical Internet Research. 26. e53863–e53863. 4 indexed citations
4.
Bonakala, Satyanarayana, Michaël Aupetit, Halima Bensmail, & Fedwa El‐Mellouhi. (2024). A human-in-the-loop approach for visual clustering of overlapping materials science data. Digital Discovery. 3(3). 502–513. 2 indexed citations
5.
Jeon, Hyeon, et al.. (2023). Classes are Not Clusters: Improving Label-Based Evaluation of Dimensionality Reduction. IEEE Transactions on Visualization and Computer Graphics. 30(1). 781–791. 9 indexed citations
6.
Peltonen, Jaakko, et al.. (2020). Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction. Trepo - Institutional Repository of Tampere University. 33. 13214–13225. 3 indexed citations
7.
Perez-Pozuelo, Ignacio, Bing Zhai, João Palotti, et al.. (2020). The future of sleep health: a data-driven revolution in sleep science and medicine. npj Digital Medicine. 3(1). 42–42. 183 indexed citations
8.
Palotti, João, Raghvendra Mall, Michaël Aupetit, et al.. (2019). Benchmark on a large cohort for sleep-wake classification with machine learning techniques. npj Digital Medicine. 2(1). 50–50. 63 indexed citations
9.
Fernández-Luque, Luis, Meghna Singh, Ferda Ofli, et al.. (2017). Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar. BMC Medical Informatics and Decision Making. 17(1). 37–37. 23 indexed citations
10.
Rawi, Reda, Raghvendra Mall, Khalid Kunji, et al.. (2016). COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator. BMC Bioinformatics. 17(1). 533–533. 2 indexed citations
11.
Aupetit, Michaël, et al.. (2015). Exploratory digraph navigation using A. HAL (Le Centre pour la Communication Scientifique Directe). 1624–1630. 1 indexed citations
12.
Verleysen, Michel, et al.. (2010). Recent Advances in Nonlinear Dimensionality Reduction, Manifold and Topological Learning. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 71–80. 39 indexed citations
13.
Lespinats, Sylvain & Michaël Aupetit. (2010). Mapping without visualizing local default is nonsense. The European Symposium on Artificial Neural Networks. 1 indexed citations
14.
Aupetit, Michaël. (2008). Homogeneous bipartition based on multidimensional ranking. The European Symposium on Artificial Neural Networks. 73(5 Pt 2). 259–264. 2 indexed citations
15.
Aupetit, Michaël. (2006). Visualizing the trustworthiness of a projection.. The European Symposium on Artificial Neural Networks. 271–276. 5 indexed citations
16.
Aupetit, Michaël. (2005). Learning Topology with the Generative Gaussian Graph and the EM Algorithm. Neural Information Processing Systems. 18. 83–90. 15 indexed citations
17.
Aupetit, Michaël. (2003). High-dimensional labeled data analysis with Gabriel graphs. The European Symposium on Artificial Neural Networks. 15(11). 21–26. 4 indexed citations
18.
Aupetit, Michaël. (2003). Robust Topology Representing Networks.. The European Symposium on Artificial Neural Networks. 45–50. 4 indexed citations
19.
Aupetit, Michaël. (2000). Function Approximation with Continuous Self-Organizing Maps Using Neighboring Influence Interpolation. 3 indexed citations
20.
Aupetit, Michaël, et al.. (1999). C-SOM: A Continuous Self-Organizing Map for Function Approximation. 2 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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