Transformation of feature space based on Fisher’s linear discriminant

被引：2
|
作者：
Nemirko A.P. [1 ]
机构：
[1] Saint Petersburg Electrotechnical University, the Department of Biotechnical Systems, Saint Petersburg
来源：
Pattern Recognition and Image Analysis | 2016年 / 26卷 / 2期
关键词：
the classic Fisher’s linear discriminant;
D O I：
10.1134/S1054661816020127
中图分类号：
学科分类号：
摘要：
Linear transformation of data in multidimensional feature space based on Fisher’s criterion is considered. The case of two classes with arbitrary distributions is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Example offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher’s linear discriminant. © 2016, Pleiades Publishing, Ltd.
引用
收藏
页码：257 / 261
页数：4
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