Abstract #301773

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JSM 2002 Abstract #301773
Activity Number: 362
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing*
Abstract - #301773
Title: Projection Pursuit Indices for Supervised Classification
Author(s): Eun-kyung Lee*+ and Dianne Cook
Affiliation(s): Iowa State University and Iowa State University
Address: 215 Sinclair Ave. #115, Ames, Iowa, 50014, USA
Keywords: Projection Pursuit ; Linear Discriminant Analysis ; Classification Trees ; Multivariate data ; Data Mining
Abstract:

In high-dimensional data, one often seeks a few interesting low-dimensional projections which reveal important aspects 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, and hence cannot be adequately applied to supervised classification problems. We introduce new indices derived from linear discriminant analysis and classification tree that can be used for exploratory supervised classification.

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