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

Authors:
Pierre Gaillard;Michaël Aupetit;Gérard Govaert
Affiliations:
Commissariat í l'Energie Atomique (CEA)-DAM-Département Analyse Surveillance Environnement, BP 12, 91680 Bruyères-le-Chítel, France;Commissariat í l'Energie Atomique (CEA)-DAM-Département Analyse Surveillance Environnement, BP 12, 91680 Bruyères-le-Chítel, France;Compiègne University of Technology, HEUDIASYC, UMR CNRS 6599, BP 20529, 60205 Compiegne Cedex, France
Venue:
Neurocomputing
Year:
2008

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Abstract

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.