Fast and Robust Approximation of Smallest Enclosing Balls in Arbitrary Dimensions

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dc.contributor.authorLarsson, Thomasen_US
dc.contributor.authorKällberg, Linusen_US
dc.contributor.editorYaron Lipman and Hao Zhangen_US
dc.date.accessioned2015-02-28T15:50:34Z
dc.date.available2015-02-28T15:50:34Z
dc.date.issued2013en_US
dc.description.abstractIn 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.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12176en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectComputer Graphics [I.3.5]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectGeometric algorithmsen_US
dc.subjectlanguagesen_US
dc.subjectand systemsen_US
dc.subjectAnalysis of algorithms and problem complexity [F.2.2]en_US
dc.subjectNonnumerical Algorithms and Problemsen_US
dc.subjectGeometrical problems and computationsen_US
dc.titleFast and Robust Approximation of Smallest Enclosing Balls in Arbitrary Dimensionsen_US
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32-Issue 5
SGP13: Eurographics Symposium on Geometry Processing (CGF 32-5)