Identifying important input variables by applying alignment in kernel Fisher discriminant analysis

摘要

Kernel Fisher discriminant analysis (KFDA) is a recent nonlinear extension of discriminant analysis. We apply KFDA to a South African coronary heart disease risk factor data set. A new measure of variable importance in KFDA is introduced, and successfully used to rank the risk factors in order of importance.