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Improved pruning of large data sets for the minimum enclosing ball problem
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-6969-6793
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1550-0994
2014 (English)In: Graphical Models, ISSN 1524-0703, E-ISSN 1524-0711, Vol. 76, no 6, 609-619 p.Article in journal (Refereed) Published
Abstract [en]

Minimum enclosing ball algorithms are studied extensively as a tool in approximation and classification of multidimensional data. We present pruning techniques that can accelerate several existing algorithms by continuously removing interior points from the input. By recognizing a key property shared by these algorithms, we derive tighter bounds than have previously been presented, resulting in twice the effect on performance. Furthermore, only minor modifications are required to incorporate the pruning procedure. The presented bounds are independent of the dimension, and empirical evidence shows that the pruning procedure remains effective in dimensions up to at least 200. In some cases, performance improvements of two orders of magnitude are observed for large data sets. © 2014 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
2014. Vol. 76, no 6, 609-619 p.
Keyword [en]
Acceleration techniques, Bounding spheres, Culling, Minimum enclosing balls, Pruning
National Category
Computer and Information Science Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-25799DOI: 10.1016/j.gmod.2014.06.003ISI: 000347018500050Scopus ID: 2-s2.0-84905501009OAI: oai:DiVA.org:mdh-25799DiVA: diva2:739144
Available from: 2014-08-20 Created: 2014-08-20 Last updated: 2016-01-18Bibliographically approved

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