Metric graph reconstruction from noisy data

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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Communication dans un congrès
27th Annual Symposium on Computational Geometry, 2011, Paris, France. pp.37-46, 2011, <10.1145/1998196.1998203>


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Dernière modification le : lundi 5 octobre 2015 - 16:56:43
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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, 2011, <10.1145/1998196.1998203>. <inria-00630774>

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