Dimensionality Reduction In Quadratic Discriminant-Analysis

    Authors

    J. R. Schott

    Comments

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    Abbreviated Journal Title

    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.

    Journal 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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