Metric graph reconstruction from noisy data

Mridul Aanjaneya 1 Frédéric Chazal 2 Daniel Chen 1 Marc Glisse 2 Leonidas J. Guibas 1 Dmitriy Morozov 3
1 Computer Science Department [Stanford]
2 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
3 LBNL - Lawrence Berkeley National Laboratory [Berkeley]
Abstract : 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.
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Submitted on : Monday, October 10, 2011 - 10:58:43 PM
Last modification on : Friday, February 23, 2018 - 2:20:05 PM
Long-term archiving on : Tuesday, November 13, 2012 - 3:36:16 PM

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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⟩

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