Known as the "kernel trick", explicitly evaluating k γ (m, m ) implicitly (i) maps the two sensor response vectors to the Hilbert space H via the feature map φ γ , then (ii) takes the dot product of these two vectors in this Hilbert space. Loosely, we may interpret the kernel as a similarity metric between two response vectors.