Degree Type

Dissertation

Date of Award

2003

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dianne H. Cook

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 can be adequately applied to supervised classification problems. We introduce new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.;When we have the small number of observations relative to the number of variables, the class structure of optimal projection can be biased too much. In this situation, most of classical multivariate analysis methods also be problematic, too. We discuss how the sample size and dimensionality are related, and we propose a new projection pursuit index that considers the penalty for the projection coefficients and overcomes the small number of observation problem.

DOI

https://doi.org/10.31274/rtd-180813-10980

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu

Copyright Owner

Eun-kyung Lee

Language

en

Proquest ID

AAI3105084

File Format

application/pdf

File Size

81 pages

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