What is the role of eigenvalues in kernel SVM interpretation?

Eigenvalues of kernel matrix describe variance distribution in feature space.

Kernel matrices are symmetric positive semi-definite and can be decomposed into eigenvalues and eigenvectors. Large eigenvalues represent dominant directions in transformed feature space. This is important in kernel PCA and helps understand complexity of SVM decision boundaries.