The problem of finding the centers and scattering matrices for a finite set of points containing outliers in a multidimensional space is considered. A new approach is considered in which instead of the arithmetic mean, differentiable mean values are used that are insensitive to outliers. An iterative reweighting scheme for searching for centers and corresponding scattering matrices for the Mahalanobis distance is considered. The examples presented in the article show the robustness property of the proposed method and algorithm with respect to a large number of outliers.
A new approach to robust clustering based on the search for cluster centers is proposed. It is based on minimizing the robust estimates of the averages and the sum of the functions of pseudo-distances to cluster centers. An algorithm of iterative reweighing type for finding cluster centres is proposed. Examples are given showing the stability of the method with respect to a large number of outliers.
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