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dc.contributor.advisorJenssen, Robert
dc.contributor.authorVikjord, Vidar Vangen
dc.description.abstractThe machine learning field based on information theory has received a lot of attention in recent years. Through kernel estimation of the probability density functions, methods developed with information theoretic measures are able to use all the statistical information available in the data, not just a finite number of moments. However, by using kernel estimation, the methods are dependent on choosing a suitable bandwidth parameter and have trouble dealing with data which vary on different scales. In this thesis, the field of information theoretic learning has been explored using k-nearest neighbor estimates for the probability density functions instead. The developed estimators of the information theoretic measures was used in a clustering routine and compared with the traditional kernel estimators.Performing clustering on a range of datasets and comparing the performance, the new method proved to provide superior results without the need of tuning any parameters. The performance difference was found to be especially large when clustering datasets where groups were on different scales.en
dc.publisherUniversitetet i Tromsøen
dc.publisherUniversity of Tromsøen
dc.rights.holderCopyright 2012 The Author(s)
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425en
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425en
dc.titleInformation theoretic learning with K nearest neighbors : a new clustering algorithmen
dc.typeMaster thesisen

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Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)