Learning Topology with the Generative Gaussian Graph and the EM Algorithm

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Michaël Aupetit


Given a set of points and a set of prototypes representing them, how to create a graph of the prototypes whose topology accounts for that of the points? This problem had not yet been explored in the framework of statistical learning theory. In this work, we propose a generative model based on the Delaunay graph of the prototypes and the ExpectationMaximization algorithm to learn the parameters. This work is a first step towards the construction of a topological model of a set of points grounded on statistics.