NONPARAMETRIC ALGORITHM OF IDENTIFICATION OF CLASSES CORRESPONDING TO SINGLE-MODE FRAGMENTS OF THE PROBABILITY DENSITY OF MULTIDIMENSIONAL RANDOM VARIABLES
A. V. Lapko1,2, V. A. Lapko1,2, S. T. Im3,2, V. P. Tuboltsev2, V. A. Avdeenok2
1Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, Russia
2Reshetnev Siberian University of Science and Technology, Krasnoyarsk, Russia
3Sukachev Institute of Forest, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, Russia
Keywords: автоматическая классификация, многомерная гистограмма, распознавание образов, выборки большого объёма, дискретизация области значений многомерных случайных величин, данные дистанционного зондирования, automatic classification, multidimensional histogram, pattern recognition, large-volume samplings, discretization of the domain of the values of multidimensional random variables, remote sensing data
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.
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