Authors: Czogiel, Irina
Luebke, Karsten
Weihs, Claus
Zentgraf, Marc
Title: Localized Linear Discriminant Analysis
Language (ISO): en
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.
Subject Headings: Classification
LDA
Local models
URI: http://hdl.handle.net/2003/22206
http://dx.doi.org/10.17877/DE290R-14249
Issue Date: 2006-02-27T14:17:12Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

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