Reducing the dimensionality of feature space in pattern recognition tasks

Abstract

The definition of an informative set of features is one of the important tasks in pattern recognition. Typically, the determination of informative features is carried out using two types of methods. The first type of methods is “direct methods”, they are directly aimed at identifying informative sets of attributes. And the second type is called "inverse methods", these methods serve to build informative sets of signs by eliminating uninformative signs from the attribute space. This article is devoted specifically to the development of the second type of method; it proposes an accelerated method and an algorithm for determining non-informative features based on the selected non-informative criterion.

Methods for assessing risk factors to the health of highly qualified athletes

“…In the mathematical modeling of the issue under study were used the methods proposed in [9]. To determine the most important risk factors that have the greatest impact on health sportsman, the methods of decreasing the dimensionality of the initial attribute space for describing objects described in [4,[10][11][12][13] were applied. To solve the above problems, the intelligent data analysis system SITO-PC was used, developed by the authors of this article and implemented in an integrated development environment programmatically Delphi 10 Seattle software.…”

“…The work uses the following methods: intellectual data analysis [1][2][3][4][5][6][7], feature type conversion [8,9], interval analysis methods [10,11], a technique for searching for hidden patterns in the initial information [12,13], questionnaires of athletes and coaches, sports tests, statistical methods.…”

Methods for improving contrast of agricultural images

“…Formation of features [9]. In this case, a set of features is formed that ensures the originality of the image, and a set of informative features is determined [10][11][12][13][14][15][16][17][18][19][20][21].…”