How does the curse of dimensionality affect kernel methods?

Kernel methods degrade because kernel values become uniformly similar.

In high dimensions, radial basis kernels (like RBF) lose contrast because pairwise distances concentrate. As a result, kernel matrices become nearly diagonal or nearly constant, reducing representational power and making SVM boundaries unstable.