Metric graph reconstruction from noisy data - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2011

Metric graph reconstruction from noisy data

Mridul Aanjaneya
  • Function : Author
  • PersonId : 842740
Frédéric Chazal
Daniel Chen
  • Function : Author
  • PersonId : 911696
Marc Glisse
Leonidas J. Guibas
  • Function : Author
  • PersonId : 850076


Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs. Building on the notions of correspondence and Gromov-Hausdorff distance in metric geometry, we describe a model for such data sets as an approximation of an underlying metric graph. We present a novel algorithm that takes as an input such a data set, and outputs the underlying metric graph with guarantees. We also implement the algorithm, and evaluate its performance on a variety of real world data sets.
Fichier principal
Vignette du fichier
ijcga.pdf (695.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00630774 , version 1 (10-10-2011)



Mridul Aanjaneya, Frédéric Chazal, Daniel Chen, Marc Glisse, Leonidas J. Guibas, et al.. Metric graph reconstruction from noisy data. 27th Annual Symposium on Computational Geometry, 2011, Paris, France. pp.37-46, ⟨10.1145/1998196.1998203⟩. ⟨inria-00630774⟩


401 View
448 Download



Gmail Facebook X LinkedIn More