Benchmark. Our benchmark, consisting of three independent procedures. (1) Dimension reduction: Every dataset is mapped into a low-dimensional target space. All necessary parameters are determined by a loo-cv with a SVM. (2) Classification: Every dataset is classified by a SVM with Gaussian kernel during 100 randomization steps. A gradient descend procedure estimates all SVM parameters. (3) Cluster validation: The Davis-Bouldin-Index measures the distance between labeled clusters within the low-dimensional data.