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Regularized discriminant analysis (1989)
| Venue: | J. Amer. Statist. Assoc |
| Citations: | 468 - 2 self |
BibTeX
@ARTICLE{Friedman89regularizeddiscriminant,
author = {Jerome H. Friedman},
title = {Regularized discriminant analysis},
journal = {J. Amer. Statist. Assoc},
year = {1989},
pages = {165--175}
}
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Abstract
Linear and quadratic discriminant analysis are considered in the small sample high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample based estimate of future misclassification risk. Computationally fast implementations are presented, and the efficacy of the approach is examined through simulation studies and application to data. These studies indicate that in many circumstances dramatic gains in classification accuracy can be achieved. Submitted to Journal of the American Statistical Association
Keyphrases
discriminant analysis computationally fast implementation small sample high-dimensional setting quadratic discriminant analysis classification accuracy simulation study many circumstance dramatic gain american statistical association covariance matrix usual maximum likelihood future misclassification risk individual situation
