Discriminant Analysis by Gaussian Mixtures

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ID: 304693
1996
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Ranked #142 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
SUMMARY Fisher-Rao linear discriminant analysis (LDA) is a valuable tool for multigroup classification. LDA is equivalent to maximum likelihood classification assuming Gaussian distributions for each class. In this paper, we fit Gaussian mixtures to each class to facilitate effective classification in non-normal settings, especially when the classes are clustered. Low dimensional views are an important by-product of LDA—our new techniques inherit this feature. We can control the within-class spread of the subclass centres relative to the between-class spread. Our technique for fitting these models permits a natural blend with nonparametric versions of LDA.
Reference Key
openalex_W1614659291 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Trevor Hastie, Robert Tibshirani
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1996
DOI
10.1111/j.2517-6161.1996.tb02073.x
URL
https://doi.org/10.1111/j.2517-6161.1996.tb02073.x
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