Development of Methods and Algorithms for Dimension Reduction of Space Description for Pattern Recognition Problem

The essence of the dimensionality reduction process is the transition to a more concise set of indicators in such a way that the associated loss of information present in the source data is minimized. This article proposes methods and algorithms for reducing the dimension of the original feature description space for the problem of pattern recognition. Four types of applied problems of reducing the dimension of the analyzed feature space are defined. The mathematical model underlying the construction of one or another dimension reduction method has been determined. For each method discussed below, a step-by-step algorithm is given that allows you to quickly and efficiently translate these methods into programming languages for various types of computers.