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Two-class pattern discrimination via recursive optimization of Patrick-Fisher distance
Aladjem, M.E.;
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Volume 2,
25-29 Aug. 1996
Page(s):60
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64 vol.2
Abstract:
A method for the linear discrimination of two classes is presented. It searches for the discriminant direction which maximizes the Patrick-Fisher (PF) distance between the projected class-conditional densities. It is a nonparametric method, in the sense that the densities are estimated from the data. Since the PF distance is a highly nonlinear function, we propose a recursive optimization procedure for searching the directions corresponding to several large local maxima of the PF distance. Its novelty lies in the transformation of the data along a found direction into data with deflated maxima of PF distance and iteration to obtain the next direction. A simulation study indicates the potential of the method to find the sequence of directions with significant class separations
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