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.

2005-03-22