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Journal Articles International Journal of Computational Geometry and Applications Year : 2012

Metric Graph Reconstruction From Noisy Data

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Abstract

Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs.19 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 a metric graph that is homeomorphic to the underlying metric graph and has bounded distortion of distances. We also implement the algorithm, and evaluate its performance on a variety of real world data sets.
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Dates and versions

hal-01094867 , version 1 (14-12-2014)

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Mridul Aanjaneya, Frédéric Chazal, Daniel Chen, Marc Glisse, Leonidas J. Guibas, et al.. Metric Graph Reconstruction From Noisy Data. International Journal of Computational Geometry and Applications, 2012, 22 (4), pp.305-325. ⟨10.1142/S0218195912600072⟩. ⟨hal-01094867⟩

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