Nonparametric feature selection

Two groups of -dimensional observations of size and are known to be random vector variables from two unknown probability distribution functions [1]. A method is discussed for obtaining an -dimensional linear subspace of the observation space in which the -variate marginal distributions are most separated, based on a nonparametric estimate of probability density functions and a distance criterion. The distance used essentially is the norm of the difference between Parzen estimates of the two densities. An algorithm is developed that determines the subspace for which the distance between the two densities is maximized. Computer simulations are performed.