A generative Gaussian graph to learn the topology of a set of points

Michaël Aupetit

A generative Gaussian graph to learn the topology of a set of points

Michaël Aupetit^*

^*Corresponding author for this work

Commissariat à l’énergie atomique et aux énergies alternatives

Research output: Contribution to conference › Paper › peer-review

Abstract

We propose a generative model based on a Delaunay graph to learn the topology of a set of points. It uses the maximum likelihood principle to tune its parameters. This work is a first step towards a topological model of a set of points grounded on statistics.

Original language	English
Pages	347-354
Number of pages	8
Publication status	Published - 2005
Externally published	Yes
Event	5th Workshop on Self-Organizing Maps, WSOM 2005 - Paris, France Duration: 5 Sept 2005 → 8 Sept 2005

Conference

Conference	5th Workshop on Self-Organizing Maps, WSOM 2005
Country/Territory	France
City	Paris
Period	5/09/05 → 8/09/05

Keywords

Delaunay graph
Generative model
Maximum likelihood
Mixture model
Topology modelling

Cite this

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title = "A generative Gaussian graph to learn the topology of a set of points",

abstract = "We propose a generative model based on a Delaunay graph to learn the topology of a set of points. It uses the maximum likelihood principle to tune its parameters. This work is a first step towards a topological model of a set of points grounded on statistics.",

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author = "Micha{\"e}l Aupetit",

year = "2005",

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KW - Delaunay graph

KW - Generative model

KW - Maximum likelihood

KW - Mixture model

KW - Topology modelling

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M3 - Paper

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T2 - 5th Workshop on Self-Organizing Maps, WSOM 2005

Y2 - 5 September 2005 through 8 September 2005

ER -

A generative Gaussian graph to learn the topology of a set of points

Abstract

Conference

Keywords

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Cite this