Information Distance in Multiples

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dc.creatorVitanyi, Paul M. B.
dc.date2009-05-20
dc.date.accessioned2026-07-07T13:16:52Z
dc.date.available2026-07-07T13:16:52Z
dc.descriptionInformation distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering, and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minimal overlap, additivity, and normalized information distance in multiples. We use the theoretical notion of Kolmogorov complexity which for practical purposes is approximated by the length of the compressed version of the file involved, using a real-world compression program. {\em Index Terms}-- Information distance, multiples, pattern recognition, data mining, similarity, Kolmogorov complexity
dc.descriptionLateX 14 pages, Submitted to a technical journal
dc.identifierhttps://arxiv.org/abs/0905.3347
dc.identifierhttp://arxiv.org/abs/0905.3347
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/230927
dc.subjectComputer Vision and Pattern Recognition
dc.subjectMachine Learning
dc.subjectJ.3; E.4
dc.titleInformation Distance in Multiples
dc.typetext

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