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Further consideration of sample and feature size (Corresp.)

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1 Author(s)

It is shown that in the context of a specific pattern classification decision metric the number of samples M needed to characterize a cluster described by N features is M \geq (1 + \beta ^{-1})(N + 2) where \beta represents an interval width. The distance metric d^{2}(X)=(X-\hat{\mu}_{x})^{t}S_{x}^{-l}(X-\hat{\mu}_{x}) is shown to have an F -distribution which leads to the result for M . An additional application of the distribution of d^{2}(X) is discussed in terms of a specific type of pattern classifier.

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Information Theory, IEEE Transactions on  (Volume:24 ,  Issue: 6 )