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dc.contributor.advisorJenssen, Robert
dc.contributor.advisorBianchi, Filippo Maria
dc.contributor.advisorSigurd, Løkse
dc.contributor.authorFoslid, Tobias Olsen
dc.date.accessioned2017-08-17T11:41:45Z
dc.date.available2017-08-17T11:41:45Z
dc.date.issued2017-05-16
dc.description.abstractThis thesis aims to apply the Dirichlet process mixture model to the cluster kernel framework. The probabilistic cluster kernel is extended with a Bayesian nonparametric model to avoid critical parameters within the model. The Dirichlet process cluster kernel demonstrate advantages compared to the probabilistic cluster kernel in both classification and clustering. Additionally, the two dimensional projection using kernel PCA and the Dirichlet process cluster kernel show compact clusters with a higher degree of cluster discrimination. The second main contribution of the thesis is an application of the cluster kernel methodology in semi-supervised learning. The Dirichlet process cluster kernel demonstrates a high degree of descriptive representation.en_US
dc.identifier.urihttps://hdl.handle.net/10037/11307
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2017 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)en_US
dc.subject.courseIDSTA-3900
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Statistikk: 412en_US
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412en_US
dc.titleDirichlet process cluster kernelen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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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)