Learning Topology with the Generative Gaussian Graph and the EM Algorithm

Michael Jean-Marie  Aupetit

Learning Topology with the Generative Gaussian Graph and the EM Algorithm

CEA

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	NIPS'05: Proceedings of the 18th International Conference on Neural Information Processing Systems
Publication status	Published - 2005
Externally published	Yes

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title = "Learning Topology with the Generative Gaussian Graph and the EM Algorithm",

abstract = "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.",

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AB - 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.

M3 - Conference contribution

BT - NIPS'05: Proceedings of the 18th International Conference on Neural Information Processing Systems

ER -

Learning Topology with the Generative Gaussian Graph and the EM Algorithm

Abstract

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