Improving Angle Based Mappings
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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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.