Extensions to Fishers Linear Discriminant Function

摘要

This Memorandum describes how Fisher's Linear Discriminant can be combined with the Fukunaga-Koontz transform to give a useful technique for reduction of feature space from many to two or three dimensions. Performance is seen to be superior in general to the Foley-Sammon extension to fisher's method. The technique is then extended 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 single projection. Keywords include: Index Terms; Dimensionality reduction, Discriminant vectors, Feature selection, Fisher criterion, Linear transformations, Separability. (Great Britain)