Projection pursuit for high dimensional feature reduction: parallel and sequential approaches

Two parametric projection pursuit algorithms have been presented, and an iterative procedure of the sequential approach that mitigates the computation time problem is shown.

Abstract

Supervised classification techniques use labeled samples in order to train the classifier. Usually the number of such samples is limited, and as the number of bands available increases, this limitation becomes more severe, and can become dominate over the projected added value of having the additional bands available. This suggests the need for reducing the dimensionality via a preprocessing method. Such reduction should enable the estimation of feature extraction parameters to be more accurate. Using a technique referred to as projection pursuit, two parametric projection pursuit algorithms have been developed: parallel parametric projection pursuit and sequential parametric projection pursuit. In the present paper both methods are presented, and an iterative procedure of the sequential approach that mitigates the computation time problem is shown.