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.

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