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Journal of Classification
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24-07-2019

Unequal Priors in Linear Discriminant Analysis

Authors: Carmen van Meegen, Sarah Schnackenberg, Uwe Ligges

Published in: Journal of Classification | Issue 3/2020

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Abstract

Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations.
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Metadata
Title
Unequal Priors in Linear Discriminant Analysis
Authors
Carmen van Meegen
Sarah Schnackenberg
Uwe Ligges
Publication date
24-07-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 3/2020
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09336-2

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