Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25045
Authors: 
Lee, Eun-Kyung
Cook, Dianne
Klinke, Sigbert
Lumley, Thomas
Year of Publication: 
2005
Series/Report no.: 
SFB 649 discussion paper 2005,026
Abstract: 
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.
Subjects: 
Data mining
Exploratory multivariate data analysis
Gene expression data
Discriminant analysis
Document Type: 
Working Paper

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