Regularized Discriminant Analysis
作者:
JeromeH. Friedman,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1989)
卷期:
Volume 84,
issue 405
页码: 165-175
ISSN:0162-1459
年代: 1989
DOI:10.1080/01621459.1989.10478752
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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.
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