On Extensions to Fisher's Linear Discriminant Function
I.D. Longstaff · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1987
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.