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- W2022382465 abstract "Abstract Single class discrimination (SCD) has recently been described for the analysis of multivariate embedded data. It is a method for determining informative axes in the data space which promote clustering of the embedded, or principal, class about the model origin and dispersal of the non-embedded class. Significance testing of the eigenvalues obtained in a model has been carried out by randomizing the class membership vector and recalculating the SCD model 500 times. These random simulations enable the determination of the permutation distribution under the null hypothesis of no association, and hence can be used to determine the significance of the first eigenvalue. A method is described to estimate the permutation distribution of the second and subsequent eigenvalues conditional on the fact that the previous eigenvectors in the SCD model have been accepted as significant." @default.
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- W2022382465 date "1994-04-01" @default.
- W2022382465 modified "2023-09-23" @default.
- W2022382465 title "Significance testing of single class discrimination models" @default.
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- W2022382465 doi "https://doi.org/10.1016/0169-7439(93)e0074-e" @default.
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