TY - JOUR
T1 - Nonparametric discriminant analysis via recursive optimization of Patrick-Fisher distance
AU - Aladjem, Mayer E.
N1 - Funding Information:
Manuscript received March 16, 1996; revised November 25, 1996. This work was supported in part by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University of the Negev, Israel. The author is with the Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel (e-mail: [email protected]). Publisher Item Identifier S 1083-4419(98)00216-7.
PY - 1998/12/1
Y1 - 1998/12/1
N2 - 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 the PF distance and iteration to obtain the next direction. A simulation study and a medical data analysis indicate the potential of the method to find the sequence of directions with significant class separations.
AB - 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 the PF distance and iteration to obtain the next direction. A simulation study and a medical data analysis indicate the potential of the method to find the sequence of directions with significant class separations.
UR - http://www.scopus.com/inward/record.url?scp=0003310707&partnerID=8YFLogxK
U2 - 10.1109/3477.662771
DO - 10.1109/3477.662771
M3 - Article
C2 - 18255948
AN - SCOPUS:0003310707
SN - 1083-4419
VL - 28
SP - 292
EP - 299
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 2
ER -