The paper presents the results on the dimensionality reduction technique which is based on radial basis function (RBF) theory. The technique uses RBF for mapping multidimensional data points into a low-dimensional space by interpolating the previously calculated position of so-called control points. This paper analyses various ways of selection of control points (regularized orthogonal least squares method, random and stratified selections). The experiments have been carried out with 8 real and artificial data sets. Positions of the control points in a low-dimensional space are found by principal component analysis. Combinations of RBF technique with random and stratified selections outperformed RBF with regularized orthogonal least squares algorithm regarding to computation time analysing all data sets. We demonstrate that random and stratified selections of control points are efficient and acceptable in terms of balance between projection error (stress) and time-consumption.