@inproceedings{61b4786f45a54d4d95f723d3adf666d4,

title = "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 [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.",

keywords = "Inference, Metric graph, Noise, Reconstruction",

author = "Mridul Aanjaneya and Frederic Chazal and Daniel Chen and Marc Glisse and Leonidas Guibas and Dmitriy Morozov",

year = "2011",

doi = "10.1145/1998196.1998203",

language = "English (US)",

isbn = "9781450306829",

series = "Proceedings of the Annual Symposium on Computational Geometry",

publisher = "Association for Computing Machinery",

pages = "37--46",

booktitle = "Proceedings of the 27th Annual Symposium on Computational Geometry, SCG'11",

}