Nonparametric Algorithm of Identification of Classes Corresponding to Single-mode Fragments of the Probability Density of Multidimensional Random Variables

Abstract

A nonparametric algorithm of automatic classification of large arrays of statistical data is considered. Its synthesis is based on decomposition of initial data. The results of decomposition form a set of centers of multidimensional intervals and the corresponding frequencies of occurrence of values of random variables. Based on information obtained, classes corresponding to single-mode fragments of the probability density of features of examined objects are detected. The spatial interpretation of automatic classification results is analyzed. The nonparametric algorithms developed in the study are important tools of processing of data obtained by remote sensing of natural resources.