Showing papers on "Dimensionality reduction published in 1987"
TL;DR: Extensions to Fisher's linear discriminant function which allow both differences in class means and covariances to be systematically included in a process for feature reduction are described.
Abstract: This correspondence describes extensions to Fisher's linear discriminant function which allow both differences in class means and covariances to be systematically included in a process for feature reduction. It is shown how the Fukunaga-Koontz transform can be combined with Fisher's method to allow a reduction of feature space from many dimensions to two. Performance is seen to be superior in general to the Foley-Sammon method. The technique is developed to show how a new radius vector (or pair of radius vectors) can be combined with Fisher's vector to produce a classifier with even more power of discrimination. Illustrations of the technique show that good discrimination can be obtained even if there is considerable overlap of classes in any one projection.
25 citations
TL;DR: It is shown that great simplicity is obtained by identifying and eliminating the least desirable feature out of the original feature space by using J-divergence as a measure of the discrimination between the classes.
9 citations
TL;DR: The feature selection problem as a task of a transformation of an initial pattern space into a new space, optimal with respect to the discriminatory features is described and the use of some conception of interclass scatter matrix calculation allows us to obtain different variations of many-class Fisher measures.
7 citations