Localized Linear Discriminant Analysis

dc.contributor.author	Czogiel, Irina
dc.contributor.author	Luebke, Karsten
dc.contributor.author	Weihs, Claus
dc.contributor.author	Zentgraf, Marc
dc.date.accessioned	2006-02-27T14:17:12Z
dc.date.available	2006-02-27T14:17:12Z
dc.date.issued	2006-02-27T14:17:12Z
dc.description.abstract	Abstract. Despite its age, the Linear Discriminant Analysis performs well even in situations where the underlying premises like normally distributed data with constant covariance matrices over all classes are not met. It is, however, a global technique that does not regard the nature of an individual observation to be classified. By weighting each training observation according to its distance to the observation of interest, a global classifier can be transformed into an observation specific approach. So far, this has been done for logistic discrimination. By using LDA instead, the computation of the local classifier is much simpler. Moreover, it is ready for applications in multi-class situations.	en
dc.format.extent	2042252 bytes
dc.format.extent	300018 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/22206
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-14249
dc.language.iso	en
dc.subject	Classification	en
dc.subject	LDA	en
dc.subject	Local models	en
dc.subject.ddc	004
dc.title	Localized Linear Discriminant Analysis	en
dc.type	Text
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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