Resource title

Localized Linear Discriminant Analysis

Resource image

image for OpenScout resource :: Localized Linear Discriminant Analysis

Resource description

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.

Resource author

Irina Czogiel, Karsten Luebke, Marc Zentgraf, Claus Weihs

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22653

Resource license

Adapt according to the presented license agreement and reference the original author.