Kernel ridge regression

Kernel ridge regression minimizes empirical squared error plus the squared norm in a Reproducing-kernel Hilbert space. The representer theorem reduces it to ridge regression with the kernel matrix as its feature Gram matrix.

For kernel matrix $K$ and objective normalized as $n^{-1}\Vert Y-f{\Vert }_2^2+\lambda \Vert f{\Vert }_{\mathcal{H}}^2$, the fitted values are $H_{\lambda }Y$, where