  The curse of dimensionality has been studied in different aspects. However,
breaking the curse has been elusive. We show for the first time that it is
possible to break the curse using the recently introduced Isolation Kernel. We
show that only Isolation Kernel performs consistently well in indexed search,
spectral & density peaks clustering, SVM classification and t-SNE visualization
in both low and high dimensions, compared with distance, Gaussian and linear
kernels. This is also supported by our theoretical analyses that Isolation
Kernel is the only kernel that has the provable ability to break the curse,
compared with existing metric-based Lipschitz continuous kernels.
