Metric Graph Reconstruction from Noisy Data

被引：0

作者：

Aanjaneya, Mridul ^{[1
]}

Chazal, Frederic

Chen, Daniel ^{[1
]}

Glisse, Marc

Guibas, Leonidas ^{[1
]}

Morozov, Dmitriy ^{[1
]}

机构：

[1] Stanford Univ, Stanford, CA 94305 USA

来源：

COMPUTATIONAL GEOMETRY (SCG 11) | 2011年

基金：

美国国家科学基金会;

关键词：

Reconstruction; metric graph; noise; inference;

D O I：

暂无

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 [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.

引用

页码：37 / 46

页数：10

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