Geometric and Topological Inference - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Ouvrage (Y Compris Édition Critique Et Traduction) Année : 2018

Geometric and Topological Inference

Jean-Daniel Boissonnat
  • Fonction : Auteur
  • PersonId : 830857
Frédéric Chazal
Mariette Yvinec

Résumé

Geometric and topological inference deals with the retrieval of information about a geometric object that is only known through a finite set of possibly noisy sample points. Geometric and topological inference employs many tools from Computational Geometry and Applied Topology. It has connections to Manifold Learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of Topological Data Analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this book covers various aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. This first book on the subject can serve for teaching in a mathematical or computer science department, and will benefit to scientists and engineers interested in a geometric approach to Data Science.
Fichier principal
Vignette du fichier
0-main.pdf (10.6 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01615863 , version 1 (12-10-2017)
hal-01615863 , version 2 (30-05-2018)

Identifiants

  • HAL Id : hal-01615863 , version 2

Citer

Jean-Daniel Boissonnat, Frédéric Chazal, Mariette Yvinec. Geometric and Topological Inference. Cambridge University Press, 2018. ⟨hal-01615863v2⟩
4375 Consultations
3078 Téléchargements

Partager

Gmail Facebook X LinkedIn More