@inproceedings{c12c9e66dd9e44a1af50fe685c6e72f2,
title = "Two-class pattern discrimination via recursive optimization of Patrick-Fisher distance",
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.",
author = "Aladjem, {Mayer E.}",
year = "1996",
month = jan,
day = "1",
doi = "10.1109/ICPR.1996.546724",
language = "English",
isbn = "081867282X",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "60--64",
booktitle = "Track B",
address = "United States",
note = "13th International Conference on Pattern Recognition, ICPR 1996 ; Conference date: 25-08-1996 Through 29-08-1996",
}