Kernel PCA depends on the number of principal components q and the width of the Gaussian kernel . For the ring-line-square distribution, the quality measure and the fractional variance, explained by the principal subspace in feature space, increased with increasing q (table 5.1). A limit in the quality was reached at about 30 principal components. For 20 principal components, the covered variance also increased with the width . However, the quality was almost constant about the tested values, with a slight peak at = 0.5 (table 5.2). For the same parameters, the results for the vortex distribution were similar. Here, the optimum was at about = 0.1. The reason for the difference is the smaller variance of the vortex distribution, which requires the optimal to be smaller.