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

Abstract. Discovering the topology of a set of labeled data in a Euclidian space can help to design better decision systems. In this work, we propose a supervised generative model based on the Delaunay Graph of some prototypes representing the labeled data.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Pierre Gaillard, Michaël Aupetit, Gérard Govaert
• Keywords: Better Decision Systems, Delaunay Graph, ESANN 2007, Neural Networks, Supervised Generative Model