Interestingly, the feature map τ (x) corresponding to the RBF kernel in eqn. maps vectors into an infinite dimensional feature space! By implicitly operating in a higher-dimensional feature space, nonlinear kernels give GPs more flexibility, or expressiveness, for approximating complicated objective functions f (x). Notably, graph kernels [ ] and image kernels [ ] can define similarities of two NPMs represented as graphs (nodes: atoms, edges: bonds) and images, respectively.