The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An Optimal Set of Discriminant Vectors
Abstract
A new method for the extraction of features in a two-class pattern recognition problem is derived. The main advantage is that the method for selecting features is based entirely upon discrimination or separability as opposed to the more common approach of fitting. The classical example of fitting is the use of the eigenvectors of the lumped covariance matrix corresponding to the largest eigenvalues. In an analogous manner, the new technique selects discriminant vectors (or features) corresponding to the largest "discrim-values." The new method is compared to some of the more popular alternative techniques via both data-dependent and mathematical examples. In addition, a recursive method for obtaining the discriminant vectors is given.
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Citations
Cites
Classification into two Multivariate Normal Distributions with Different Covariance Matrices
Anderson, T. W., & Bahadur, R. R. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704568 · Cited by 248
Metadata
- Authors
- Foley, D. H., & Sammon, J. W.
- Venue
- IEEE Transactions on Computers
- Published
- 1975-03
- Volume
- C-24
- Issue
- 3
- Pages
- 281-289
- ISSN
- 0018-9340 (ISSN-L)
- OpenAlex
- W1971747485
- Cited by
- 406
- References
- 1
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- Foley, D. H., & Sammon, J. W. (1975). An Optimal Set of Discriminant Vectors. IEEE Transactions on Computers. https://doi.org/10.1109/t-c.1975.224208
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
