Determination of representative features when building an extreme recognition algorithm

Abstract

Abstract The problems of determining the representative features when constructing an extremal pattern recognition algorithm defined in a high-dimensional feature space are considered. As an initial model, a model of recognition algorithms based on radial functions was considered. The procedures for extracting subsets of interrelated features and selecting a set of representative features when constructing an extremal algorithm within the framework of the recognition model under consideration are presented. The main idea of the first procedure is to form a set of features that are unrelated (or are connected rather weakly) among themselves. The essence of it is as follows. The studied subsets of features are combined into one subset if they are close enough to each other in a sense. Otherwise, they belong to different subsets. The main idea of the second procedure is to define a representative element in each subset of tightly coupled features. The choice of an element from the considered subset of tightly coupled features is made on the basis of an assessment of the proximity of features. It is required that among the selected set of features there should not be tightly coupled features. The main advantage of the proposed procedures is to improve the accuracy of the results of allocating subsets of tightly coupled features when building a recognition algorithm under conditions of large dimensionality of the feature space and determining the quantitative evaluation of these subsets. In order to assess the performance of the developed procedures, experimental studies were carried out. The application of the developed procedures allows to more accurately determine the unknown parameters of recognition operators in the space of features of large dimension.