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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationWSOM 2005 - 5th Workshop on Self-Organizing Maps
Pages347-354
Number of pages8
Publication statusPublished - 2005
Externally publishedYes
Event5th Workshop on Self-Organizing Maps, WSOM 2005 - Paris
Duration: 5 Sep 20058 Sep 2005

Other

Other5th Workshop on Self-Organizing Maps, WSOM 2005
CityParis
Period5/9/058/9/05

Fingerprint

Topology
Maximum likelihood
Statistics

Keywords

  • Delaunay graph
  • Generative model
  • Maximum likelihood
  • Mixture model
  • Topology modelling

ASJC Scopus subject areas

  • Information Systems

Cite this

Aupetit, M. (2005). A generative Gaussian graph to learn the topology of a set of points. In WSOM 2005 - 5th Workshop on Self-Organizing Maps (pp. 347-354)

A generative Gaussian graph to learn the topology of a set of points. / Aupetit, Michael.

WSOM 2005 - 5th Workshop on Self-Organizing Maps. 2005. p. 347-354.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aupetit, M 2005, A generative Gaussian graph to learn the topology of a set of points. in WSOM 2005 - 5th Workshop on Self-Organizing Maps. pp. 347-354, 5th Workshop on Self-Organizing Maps, WSOM 2005, Paris, 5/9/05.
Aupetit M. A generative Gaussian graph to learn the topology of a set of points. In WSOM 2005 - 5th Workshop on Self-Organizing Maps. 2005. p. 347-354
Aupetit, Michael. / A generative Gaussian graph to learn the topology of a set of points. WSOM 2005 - 5th Workshop on Self-Organizing Maps. 2005. pp. 347-354
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