In ordinary linear regression ($c=0$) and ridge regression ($c > 0$), for design matrix $X$ with dimensions $N$ observations by $D$ dimensions, the $N \times N$ hat matrix is given by:
$$H = X (X^T X + c I)^{-1} X^T$$
If we consider the $ij$-th element, it is given by
$$[H]_{ij} = x_i^T \Big( \underbrace{\sum_n x_n x_n^T + c I }_{:= M}\Big)^{-1} x_j$$.
What effect does this $D \times D$ matrix $M$ (specifically, its inverse) have on the inner product between $x_i$ and $x_j$?
It looks similar to the Mahalanobis distance, but defined using non-centered data and the non-centered second moment rather than centered data and the second moment.