Computational Science and Techniques

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.

This paper addresses the issue of finding the most efficient estimator of the normal population mean when the population “Coefficient of Variation (C. V.)” is ‘Rather-Very-Large’ though unknown, using a small sample (sample-size ≤ 30). The paper proposes an “Efficient Iterative Estimation Algorithm exploiting sample “C. V.” for an efficient Normal Mean estimation”. The MSEs of the estimators per this strategy have very intricate algebraic expression depending on the unknown values of population parameters, and hence are not amenable to an analytical study determining the extent of gain in their relative efficiencies with respect to the Usual Unbiased Estimator X ̅(sample mean ~ Say ‘UUE’). Nevertheless, we examine these relative efficiencies of our estimators with respect to the Usual Unbiased Estimator, by means of an illustrative simulation empirical study. MATLAB 7.7.0.471 (R2008b) is used in programming this illustrative ‘Simulated Empirical Numerical Study’.

The article discusses the possibilities of Klaipeda region historic cemeteries destruction risk assessment using multiple criteria analytic hierarchy process (AHP). The proposing original assessment methodology developed by combining information from scientific literature on historical artefacts preservation topic with the data collected by scientists of Institute of Baltic Region History and Archaeology during their field expeditions to Klaipeda region Evangelical Lutheran Cemeteries.The results show that the process of historical cemeteries destruction risk assessment can be formalized and fully automated using AHP and modern software.