@inproceedings{8f5c0980707e4020a3ce3ad8c1f83158,
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",
month = jan,
day = "1",
doi = "10.1145/1998196.1998203",
language = "English",
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",
}