Robust method for finding the center and the scatter matrix of the cluster

Abstract

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.

Two Advanced Models of the Function of MRT Public Transportation in Taipei

“…The purpose of this step was to screen the input variables with a lower correlation to the Taipei MRT passenger traffic to avoid interfering with the prediction results. The regression analysis is primarily used to discuss the causal result relationship among the variables and conduct the prediction via a line chart [20,21], a scatter chart [22], correlation analysis [23][24][25], the enter method [26,27], and stepwise regression [28][29][30].…”