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Fast and robust approximation of smallest enclosing balls in arbitrary dimensions
Mälardalens högskola, Akademin för innovation, design och teknik.ORCID-id: 0000-0002-1550-0994
Mälardalens högskola, Akademin för innovation, design och teknik.ORCID-id: 0000-0002-6969-6793
2013 (engelsk)Inngår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 32, nr 5, s. 93-101Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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. 

sted, utgiver, år, opplag, sider
2013. Vol. 32, nr 5, s. 93-101
Emneord [en]
Arbitrary dimension, Empirical evaluations, General class, High dimensions, Large problems, Reliable results, Robust approximations, Theoretical arguments, Computer graphics, Approximation algorithms
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Identifikatorer
URN: urn:nbn:se:mdh:diva-21268DOI: 10.1111/cgf.12176ISI: 000323204000010Scopus ID: 2-s2.0-84882797817OAI: oai:DiVA.org:mdh-21268DiVA, id: diva2:646057
Tilgjengelig fra: 2013-09-06 Laget: 2013-09-06 Sist oppdatert: 2018-02-23bibliografisk kontrollert

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