Magnification in divergence based neural maps

In this paper, we consider the magnification behavior of neural maps using several (parametrized) divergences as dissimilarity measure instead of the Euclidean distance. We show experimentally that optimal magnification, i.e. information optimum data coding by the prototypes, can be achieved for properly chosen divergence parameters. Thereby, the divergences considered here represent all main classes of divergences. Hence, we can conclude that information optimal vector quantization can be processed independently from the divergence class by appropriate parameter setting.