Nonparametric discriminant analysis applied to medical diagnosis

摘要

The authors present an application of their method for discriminant analysis (Proc. 13th Internat. Conf. on Pattern Recognition, vol. 2, p. 60-4, 1996) to the diagnosis of the neurological diseases hemorrhages and infarction due to ischemia. The method searches for the discriminant directions which maximize 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, the authors use a recursive optimization procedure for searching the directions corresponding to several large local maxima of the PF distance. The application to the medical dataset indicates the potential of the authors' method for finding a sequence of directions with significant class separation.