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Regularized Discriminant Analysis
Open accessAbstract
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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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
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Goldstein, M., & Uchida, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152173 · Cited by 1,032
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Metadata
- Authors
- Friedman, J. H.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1989-03
- Volume
- 84
- Issue
- 405
- Pages
- 165-175
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2046649434
- Cited by
- 2,421
- References
- 1
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- green
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