Abstract
The feature selection problem as a task of a transformation of an initial pattern space into a new space, optimal with respect to the discriminatory features is described. Transformation optimizations are realized according to the measures which may be included in the broadly understood group of Fisher measures. In particular, the use of some conception of interclass scatter matrix calculation allows us to obtain different variations of many-class Fisher measures. Finally, a theoretical comparison of some properties of the suggested Fisher transformations with other transformations based on the Karhunen-Loève expansion is presented.
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