publi ambroise

_bibliography/in_production.bib

@@ -18,23 +18,4 @@ @article{giorgi2024
 }
 }
 
-@article{ambroise2024,
-  bibtex_show = {true},
-  author = {Laplante, Félix and Ambroise, Christophe},
-  publisher = {French Statistical Society},
-  title = {Spectral Bridges: Scalable Spectral Clustering Based on Vector Quantization},
-  journal = {Computo},
-  year        = 2024,
-  url = {https://computo.sfds.asso.fr/published-202412-ambroise-spectral/},
-  doi = {10.57750/1gr8-bk61},
-  issn = {2824-7795},
-  type        = {{Research article}},
-  domain      = {Machine Learning},
-  language    = {R},
-  repository  = {published-202412-ambroise-spectral},
-  langid = {en},
-  abstract = {In this paper, Spectral Bridges, a novel clustering algorithm, is introduced. This algorithm builds upon the traditional k-means and spectral clustering frameworks by subdividing data into small Voronoï regions, which are subsequently merged according to a connectivity measure. Drawing inspiration from Support Vector Machine’s margin concept, a non-parametric clustering approach is proposed, building an affinity margin between each pair of Voronoï regions. This approach delineates intricate, non-convex cluster structures and is robust to hyperparameter choice. The numerical experiments underscore Spectral Bridges as a fast, robust, and versatile tool for clustering tasks spanning diverse domains. Its efficacy extends to large-scale scenarios encompassing both real-world and synthetic datasets. The Spectral Bridge algorithm is implemented both in Python (https://pypi.org/project/spectral-bridges) and R (https://github.com/cambroise/spectral-bridges-Rpackage).
-}
-}
-
 

_bibliography/published.bib

@@ -1,3 +1,22 @@
+@article{ambroise2024,
+  bibtex_show = {true},
+  author = {Laplante, Félix and Ambroise, Christophe},
+  publisher = {French Statistical Society},
+  title = {Spectral Bridges: Scalable Spectral Clustering Based on Vector Quantization},
+  journal = {Computo},
+  year        = 2024,
+  url = {https://computo.sfds.asso.fr/published-202412-ambroise-spectral/},
+  doi = {10.57750/1gr8-bk61},
+  issn = {2824-7795},
+  type        = {{Research article}},
+  domain      = {Machine Learning},
+  language    = {R},
+  repository  = {published-202412-ambroise-spectral},
+  langid = {en},
+  abstract = {In this paper, Spectral Bridges, a novel clustering algorithm, is introduced. This algorithm builds upon the traditional k-means and spectral clustering frameworks by subdividing data into small Voronoï regions, which are subsequently merged according to a connectivity measure. Drawing inspiration from Support Vector Machine’s margin concept, a non-parametric clustering approach is proposed, building an affinity margin between each pair of Voronoï regions. This approach delineates intricate, non-convex cluster structures and is robust to hyperparameter choice. The numerical experiments underscore Spectral Bridges as a fast, robust, and versatile tool for clustering tasks spanning diverse domains. Its efficacy extends to large-scale scenarios encompassing both real-world and synthetic datasets. The Spectral Bridge algorithm is implemented both in Python (https://pypi.org/project/spectral-bridges) and R (https://github.com/cambroise/spectral-bridges-Rpackage).
+}
+}
+
 @article{legrand2024,
   bibtex_show = {true},
   author = {Legrand, Juliette and Pimont, François and Dupuy, Jean-Luc