METRIC GRAPH RECONSTRUCTION FROM NOISY DATA-Reference-Cited by-同舟云学术

METRIC GRAPH RECONSTRUCTION FROM NOISY DATA

Published:2012-08 Issue:04 Volume:22 Page:305-325
ISSN:0218-1959
Container-title:International Journal of Computational Geometry & Applications
language:en
Short-container-title:Int. J. Comput. Geom. Appl.

Author:

AANJANEYA MRIDUL¹,CHAZAL FREDERIC²,CHEN DANIEL³,GLISSE MARC²,GUIBAS LEONIDAS⁴,MOROZOV DMITRIY⁵

Affiliation:

1. Computer Science Department, Stanford University, Gates 204, 353 Serra Mall, Stanford, California 94305, USA

2. GEOMETRICA, INRIA Saclay - Île-de-France, 2-4, rue Jacques Monod, Orsay, 91893 Cedex, France

3. Computer Science Department, Stanford University, Clark S297, 318 Campus Drive, Stanford, California 94305, USA

4. Computer Science Department, Stanford University, Clark S293, 318 Campus Drive, Stanford, California 94305, USA

5. Visualization Group, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 50F-1650, Berkeley, California 94720, USA

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Geometry and Topology,Theoretical Computer Science

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218195912600072

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