Projection Pursuit Fitting Gaussian Mixture Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
An adaptive partitioning approach for mining discriminant regions in 3D image data
Journal of Intelligent Information Systems
A novel kernel-based maximum a posteriori classification method
Neural Networks
SVM decision boundary based discriminative subspace induction
Pattern Recognition
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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