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Conference papers
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.
Cited literature [16 references]
https://hal.inria.fr/inria-00630774
Contributor : Marc Glisse <>
Submitted on : Monday, October 10, 2011 - 10:58:43 PM
Last modification on : Friday, September 20, 2019 - 4:56:39 PM
Document(s) archivé(s) le : Tuesday, November 13, 2012 - 3:36:16 PM
Contributor : Marc Glisse <>
Submitted on : Monday, October 10, 2011 - 10:58:43 PM
Last modification on : Friday, September 20, 2019 - 4:56:39 PM
Document(s) archivé(s) le : Tuesday, November 13, 2012 - 3:36:16 PM
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ijcga.pdf
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