Figure 9

(a) The percentage correct classification (an indication of the clustering success) is shown with successive iteration of the clustering algorithm. Five separate test runs are shown, on different data from the same classes. Note that the plateau at around 0.9, this is approximately the performance of a supervised binary SVM on the same data (i.e., perfect separation isn't possible with this data without employing weak-data rejection). (b) The degradation in clustering performance for less optimal selection of kernel and tuning parameter (variance in case of Gaussian). (c) The degradation in clustering performance for non-optimal selection of kernel and tuning parameter (variance in case of Gaussian). (d) Summary of the degradation in clustering performance for less optimal selection of kernel and tuning parameter – with averages of the five test-runs are used as representative curves for that kernel/tuning selection in the above.