Heteroscedastic Gaussian Kernel - Based Topographic Maps

Several learning algorithms for topographic map formation have been introduced that adopt overlapping activa- tion regions, rather than Voronoiregions, usually in the form of kernel functions. We review and introduce a number of fixed point rules for training homogeneous, heteroscedastic but otherwise radially-symmetric Gaussian kernel- based topographic maps, or kernel topographic maps. We compare their performance for clustering a number of real world data sets.