Resource title

Projection pursuit for exploratory supervised classification

Resource image

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Resource description

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data. We introduce new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.

Resource author

Eun-Kyung Lee, Dianne Cook, Sigbert Klinke, Thomas Lumley

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/25045

Resource license

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