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K. Magnus Åberg and Sven P. Jacobsson On the separation of classes: can the Fisher criterion be improved upon when classes have unequal variance–covariance structure? Journal of Chemometrics 24

Version of Record online: 8 OCT 2010 | DOI: 10.1002/cem.1326

A value of class separation can be used to optimise data-analytical protocols for multivariate classification problems. The Fisher criterion assumes equal variance-covariance structure, an assumption which is often violated in real datasets. Here, we show that about fifty samples are required to benefit from using the unequal variance-covariance structures in two dimensions; higher dimensions require more samples. The Fisher criterion is robust and accurate for selection between pre-treatments compared to a newly derived Cooke criterion.

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