METRIC GRAPH RECONSTRUCTION FROM NOISY DATA

被引：23

作者：

Aanjaneya, Mridul ^{[1
]}

Chazal, Frederic ^{[2
]}

Chen, Daniel ^{[1
]}

Glisse, Marc ^{[2
]}

Guibas, Leonidas ^{[1
]}

Morozov, Dmitriy ^{[3
]}

机构：

[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

[2] INRIA Saclay Ile de France, GEOMETRICA, F-91893 Orsay, France

[3] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Visualizat Grp, Berkeley, CA 94720 USA

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL GEOMETRY & APPLICATIONS | 2012年 / 22卷 / 04期

关键词：

Reconstruction; metric graph; noise; inference; NETWORK;

D O I：

10.1142/S0218195912600072

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：305 / 325

页数：21

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