Multidimensional scaling provides low-dimensional visualisation of high-dimensional feature vectors. This is a very important step in data preprocessing because it helps the user to appraise which methods to use for further data analysis. But a well known problem with conventionalMDS is the quadratic need of space and time. Beside this, a transformation of MDS must be completely recomputed if additional feature vectors have to be considered. The POLARMAP algorithm, presented in this paper, learns a function, similar to NeuroScale, but with lower computational costs, that maps high-dimensional feature vectors to a 2- dimensional feature space. With the obtained function even new feature vectors can be mapped to the target space.