Royal Signals and Radar Establishment (RSRE), Malvern WR14 3PS, England; Western Australia Institute of Technology, Perth, Western Australia.
IEEE Trans Pattern Anal Mach Intell. 1987 Feb;9(2):321-5. doi: 10.1109/tpami.1987.4767906.
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.
这封通信描述了对 Fisher 线性判别函数的扩展，这些扩展允许系统地将类别均值和协方差的差异包含在特征降维过程中。展示了如何将 Fukunaga-Koontz 变换与 Fisher 方法结合使用，从而将多维特征空间减少到二维。一般来说，该方法的性能优于 Foley-Sammon 方法。该技术的开发表明，如何使用新的半径向量（或一对半径向量）与 Fisher 向量相结合，从而产生具有更强判别能力的分类器。该技术的说明表明，即使在任何一个投影中类别之间存在相当大的重叠，也可以获得良好的判别能力。
