K-Means as the Zero-Temperature Limit of Normalized RBF Assignments: Rates, Trajectories, and Exact Recovery with Entmax

K-Means is usually treated as a discrete procedure outside gradient-based learning, whereas normalized radial basis function (RBF) layers are smooth. We make precise the sense in which K-Means is the zero-temperature limit of such a layer. Relaxing the K-Means assignments to the simplex with a Shann