Learning topology of a labeled data set with the supervised generative Gaussian graph

Pierre Gaillard, Michael Aupetit, Gérard Govaert

Research output: Contribution to journalArticle

12 Citations (Scopus)


Extracting the topology of a set of a labeled data is expected to provide important information in order to analyze the data or to design a better decision system. In this work, we propose to extend the generative Gaussian graph to supervised learning in order to extract the topology of labeled data sets. The graph obtained learns the intra-class and inter-class connectedness and also the manifold-overlapping of the different classes. We propose a way to vizualize these topological features. We apply it to analyze the well-known Iris database and the three-phase pipe flow database.

Original languageEnglish
Pages (from-to)1283-1299
Number of pages17
Issue number7-9
Publication statusPublished - Mar 2008
Externally publishedYes



  • Delaunay graph
  • EM algorithm
  • Gabriel graph
  • Mixture models
  • Supervised topology learning
  • Topology representing graph

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cellular and Molecular Neuroscience

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