Fast and Robust Approximation of Smallest Enclosing Balls in Arbitrary Dimensions
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Date
2013
Authors
Journal Title
Journal ISSN
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Publisher
The Eurographics Association and Blackwell Publishing Ltd.
Abstract
In this paper, an algorithm is introduced that computes an arbitrarily fine approximation of the smallest enclosing ball of a point set in any dimension. This operation is important in, for example, classification, clustering, and data mining. The algorithm is very simple to implement, gives reliable results, and gracefully handles large problem instances in low and high dimensions, as confirmed by both theoretical arguments and empirical evaluation. For example, using a CPU with eight cores, it takes less than two seconds to compute a 1:001-approximation of the smallest enclosing ball of one million points uniformly distributed in a hypercube in dimension 200. Furthermore, the presented approach extends to a more general class of input objects, such as ball sets.
Description
@article{:10.1111/cgf.12176,
journal = {Computer Graphics Forum},
title = {{Fast and Robust Approximation of Smallest Enclosing Balls in Arbitrary Dimensions}},
author = {Larsson, Thomas and Källberg, Linus},
year = {2013},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/cgf.12176}
}