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Estimating perimeter using graph cuts

Part of: Nonparametric inference Discrete mathematics in relation to computer science Geometric probability and stochastic geometry

Published online by Cambridge University Press:  17 November 2017

Nicolás García Trillos ,
Dejan Slepčev  and
James von Brecht
Nicolás García Trillos*
Affiliation:
Brown University
Dejan Slepčev*
Affiliation:
Carnegie Mellon University
James von Brecht*
Affiliation:
California State University, Long Beach
*
* Postal address: Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. Email address: nicolas_garcia_trillos@brown.edu
** Postal address: Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
*** Postal address: Department of Mathematics and Statistics, California State University, Long Beach, CA 90840, USA.
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Abstract

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We investigate the estimation of the perimeter of a set by a graph cut of a random geometric graph. For Ω ⊆ D = (0, 1)d with d ≥ 2, we are given n random independent and identically distributed points on D whose membership in Ω is known. We consider the sample as a random geometric graph with connection distance ε > 0. We estimate the perimeter of Ω (relative to D) by the, appropriately rescaled, graph cut between the vertices in Ω and the vertices in D ∖ Ω. We obtain bias and variance estimates on the error, which are optimal in scaling with respect to n and ε. We consider two scaling regimes: the dense (when the average degree of the vertices goes to ∞) and the sparse one (when the degree goes to 0). In the dense regime, there is a crossover in the nature of the approximation at dimension d = 5: we show that in low dimensions d = 2, 3, 4 one can obtain confidence intervals for the approximation error, while in higher dimensions one can obtain only error estimates for testing the hypothesis that the perimeter is less than a given number.

Keywords

Perimeternonparametric estimationgraph cutpoint cloudrandom geometric graphconcentration inequality

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry
Secondary: 62G20: Asymptotic properties 68R10: Graph theory (including graph drawing)

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 49 , Issue 4 , December 2017 , pp. 1067 - 1090
Copyright
Copyright © Applied Probability Trust 2017 

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