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- Trevor Hastie
- Stanford University USA
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- Robert Tibshirani
- University of Toronto Canada
書誌事項
- 公開日
- 1996-01-01
- 権利情報
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- https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model
- DOI
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- 10.1111/j.2517-6161.1996.tb02073.x
- 公開者
- Oxford University Press (OUP)
この論文をさがす
説明
<jats:title>SUMMARY</jats:title> <jats:p>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.</jats:p>
収録刊行物
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- Journal of the Royal Statistical Society Series B: Statistical Methodology
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Journal of the Royal Statistical Society Series B: Statistical Methodology 58 (1), 155-176, 1996-01-01
Oxford University Press (OUP)