Metric graph reconstruction from noisy data

Mridul Aanjaneya, Frederic Chazal, Daniel Chen, Marc Glisse, Leonidas J. Guibas, Dmitriy Morozov
2011 Proceedings of the 27th annual ACM symposium on Computational geometry - SoCG '11  
Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs [16] . 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.
doi:10.1145/1998196.1998203 dblp:conf/compgeom/AanjaneyaCCGGM11 fatcat:llpnuprip5eczdqxfwkmccfek4