Intensional method for recognizing objects described in a high-dimensional feature space
Keywords:
intensional method, family of recognition algorithms, interconnectedness of features, parameterization, extremal algorithmAbstract
The paper proposes a method that belongs to the group of intensional methods of pattern recognition, the essence of which is the transition from a system of initial features to a reduced system of models of interconnectedness between features. It is shown that the implementation of three options for using feature interconnection models serves as the basis for the development of three families of recognition algorithms: a family of recognition algorithms based on the use of feature interconnection models; a family of recognition algorithms based on the use of preferred models of feature interconnection; a family of recognition algorithms based on identifying preferred combinations of patterns of feature interconnection. The stages of specifying the proposed families of recognition algorithms are presented, and the problem of their parameterization is formulated. For these families of algorithms, procedures have been developed that ensure the construction of extreme algorithms for solving applied recognition problems in conditions of high dimensionality of the source data. A computer experiment was conducted to compare the effectiveness of the developed algorithms with the performance of alternative known recognition methods. The experiment showed that the developed recognition algorithms, in comparison with alternatives, have higher accuracy and performance when recognizing objects in the space of correlated features.
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