The Euclidean distance classifier: an alternative to the linear discriminant function
作者:
Virgil R. Marco,
Dean M. Young,
Danny W. Turner,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1987)
卷期:
Volume 16,
issue 2
页码: 485-505
ISSN:0361-0918
年代: 1987
DOI:10.1080/03610918708812601
出版商: Marcel Dekker, Inc.
关键词: probability of correct classification;Mahalanobis distance;nonsherical normal distribution
数据来源: Taylor
摘要:
The sample linear discriminant function (LDF) is known to perform poorly when the number of features p is large relative to the size of the training samples, A simple and rarely applied alternative to the sample LDF is the sample Euclidean distance classifier (EDC). Raudys and Pikelis (1980) have compared the sample LDF with three other discriminant functions, including thesample EDC, when classifying individuals from two spherical normal populations. They have concluded that the sample EDC outperforms the sample LDF when p is large relative to the training sample size. This paper derives conditions for which the two classifiers are equivalent when all parameters are known and employs a Monte Carlo simulation to compare the sample EDC with the sample LDF no only for the spherical normal case but also for several nonspherical parameter configurations. Fo many practical situations, the sample EDC performs as well as or superior to the sample LDF, even for nonspherical covariance configurations.
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