ON EXTENSIONS TO FISHER'S LINEAR DISCRIMINANT FUNCTION.

被引：0

作者：

Longstaff, Ian D. ^{[1
]}

机构：

[1] Western Australia Inst of Technology, Perth, Aust, Western Australia Inst of Technology, Perth, Aust

来源：

IEEE Transactions on Pattern Analysis and Machine Intelligence | 1987年 / PAMI-9卷 / 02期

关键词：

IMAGE PROCESSING - PROBABILITY;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A description is given of extensions to R. A. Fisher's (1936) linear discriminant function that 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.

引用

页码：321 / 325

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