Using piecewise hyper linear classification in multidimensional feature space for text content

Abstract

Information technology allowing to implement problems of classification, clustering, researching of topology of the information component of the data is proposed. The multidimensional feature space is reduced to the visual presentation space to determine the information content of the data. Optimized reduction of the space dimension to two-dimensional one applying multidimensional scaling methods is used. Using piecewise linear constraints allows to implement projecting into original multidimensional feature space. Visual construction of restrictive separators makes possible to consider tolerance fields of changing of features parameters, measure separation of classes, nonlinearity of data grouping. Thus, restrictive areas of hyperspace for the necessary categories of classes are formed. At the same time, visualization of classification processes in hyperspace is provided. Information technology base is multidimensional space projecting into visual (two-dimensional) space, constructing of piecewise linear constraints of studied areas, subsequent constraints projecting into multidimensional space. Thus, information technology enables to synthesize separating hyperplanes limiting categories of classes in multidimensional space.