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dc.contributor.authorWILSON, SIMON PAUL
dc.date.accessioned2013-09-04T10:00:56Z
dc.date.available2013-09-04T10:00:56Z
dc.date.issued2011
dc.date.submitted2011en
dc.identifier.citationKatarina Domijan and Simon P Wilson, Bayesian kernel projections for classification of high dimensional data, Statistics and Computing, 21, 2, 2011, 203-en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/67361
dc.descriptionPUBLISHEDen
dc.description.abstractA Bayesian multi-category kernel classification method is proposed. The algorithm performs the classification of the projections of the data to the principal axes of the feature space. The advantage of this approach is that the regression coefficients are identifiable and sparse, leading to large computational savings and improved classification performance. The degree of sparsity is regulated in a novel framework based on Bayesian decision theory. The Gibbs sampler is implemented to find the posterior distributions of the parameters, thus probability distributions of prediction can be obtained for new data points, which gives a more complete picture of classification. The algorithm is aimed at high dimensional data sets where the dimension of measurements exceeds the number of observations. The applications considered in this paper are microarray, image processing and near-infrared spectroscopy data.en
dc.description.sponsorshipWe are indebted to A. Teynor from Albert- Ludwigs-Universit?at Freiburg for providing us with the animal categorization data.en
dc.format.extent203en
dc.language.isoenen
dc.relation.ispartofseriesStatistics and Computing;
dc.relation.ispartofseries21;
dc.relation.ispartofseries2;
dc.rightsYen
dc.subject.otherBayesian
dc.titleBayesian kernel projections for classification of high dimensional dataen
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/swilson
dc.identifier.rssinternalid62599
dc.rights.ecaccessrightsOpenAccess
dc.identifier.rssurihttp://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11222-009-9161-8&sa_campaign=Email/ACE/OFen
dc.contributor.sponsorEuropean Commissionen


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