inria-00383685, version 2

## Geometric Inference for Measures based on Distance Functions

Frédéric Chazal () 1, David Cohen-Steiner () 1, Quentin Mérigot () 1

Foundations of Computational Mathematics 11, 6 (2011) 733-751

Résumé : Data often comes in the form of a point cloud sampled from an unknown compact subset of Euclidean space. The general goal of geometric inference is then to recover geometric and topological features (Betti numbers, curvatures,...) of this subset from the approximating point cloud data. In recent years, it appeared that the study of distance functions allows to address many of these questions successfully. However, one of the main limitations of this framework is that it does not cope well with outliers nor with background noise. In this paper, we show how to extend the framework of distance functions to overcome this problem. Replacing compact subsets by measures, we introduce a notion of distance function to a probability distribution in $\R^n$. These functions share many properties with classical distance functions, which makes them suitable for inference purposes. In particular, by considering appropriate level sets of these distance functions, it is possible to associate in a robust way topological and geometric features to a probability measure. We also discuss connections between our approach and non parametric density estimation as well as mean-shift clustering.

• Domaine : Informatique/Géométrie algorithmique
• Mots-clés : density estimation – reconstruction – Wasserstein distance – Mean-Shift
• Référence interne : RR-6930
• Versions disponibles :  v1 (13-05-2009) v2 (23-06-2010)

• inria-00383685, version 2
• oai:hal.inria.fr:inria-00383685
• Contributeur :
• Soumis le : Mercredi 23 Juin 2010, 14:13:07
• Dernière modification le : Jeudi 26 Septembre 2013, 13:31:06