Title

Dimensionality Reduction In Quadratic Discriminant Analysis

Keywords

Eigenprojection; Heterogeneous covariance matrices; Misclassification 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. © 1993.

Publication Date

1-1-1993

Publication Title

Computational Statistics and Data Analysis

Volume

16

Issue

2

Number of Pages

161-174

Document Type

Article

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/0167-9473(93)90111-6

Socpus ID

38249001523 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/38249001523

This document is currently not available here.

Share

COinS