The method of dividing the training sample into non-intersecting groups of objects
based on the property of their connection according to the defined subset of boundary
objects of classes is considered. Grouping is used to find the coverage of the sample with
reference objects. The formation of a new feature space for representing objects is described
by nonlinear mapping of non-intersecting set of features onto the number axis.
image recognition, logical patterns, data cluster analysis,
Space dimensional reduction is possible in the form of a recursive process of volume
unity of signs. The set of features obtained at the next step of recursion is is the initial for
the algorithm in the next step. Ideally describing class objects can be reduced to a single