Projection pursuit in high dimensional data reduction: initial conditions, feature selection and the assumption of normality

Abstract

Supervised classification techniques use labeled samples to train the classifier. Often the number of such samples is limited, thus limiting the precision with which class characteristics can be estimated. As the number of spectral bands becomes large, the limitation on performance imposed by the limited number of training samples can become severe. Such consequences suggest the value of reducing the dimensionality by a pre-processing method that takes advantage of the asymptotic normality of projected data. Using a technique called projection pursuit, a pre-processing dimensional reduction method has been developed based on the optimization of a projection index. A method to estimate an initial value that can more quickly lead to the global maximum is presented for projection pursuit using the Bhattacharyya distance as the projection index.