Title

Dimensionality Reduction In Quadratic Discriminant-Analysis

Authors

J. R. Schott

Title - Alternative

Comput. Stat. Data Anal.

Keywords

Eigenprojection; Heterogeneous Covariance Matrices; Misclassification; Probability; Canonical Variate Analysis; Confidence-Regions; Classification; Computer Science, Interdisciplinary Applications; Statistics &; Probability

Abstract

One common objective of many multivariate techniques is to achieve a reduction in dimensionality while at the same time retain most of the relevant information contained in the original data set. This reduction not only provides a parsimonious description of the data but, in many cases, also increases the reliability of subsequent analyses of the data. In this paper we consider the problem of determining the minimum dimension necessary for quadratic discrimination in normal populations with heterogeneous covariance matrices. Some asymptotic chi-squared tests are obtained. Simulations are used to investigate the adequacy of the chi-squared approximations and to compare the misclassification probabilities of reduced-dimension quadratic discrimination with full-dimension quadratic discrimination.

Publication Title

Computational Statistics & Data Analysis

Volume

16

Issue/Number

2

Publication Date

1-1-1993

Document Type

Article

Language

English

First Page

161

Last Page

174

WOS Identifier

WOS:A1993LR05700002

ISSN

0167-9473

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