Reduction of feature space dimension based on separability criterion

Abstract

Linear transformation of data in multidimensional feature space based on Fisher's criterion is considered. The case of two classes is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Examples offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher's linear discriminant.