Proves universal consistency of GW-k-NN on finite-support metric measure spaces with uniform measure and of fGW-k-NN on node-attributed versions, with competitive empirical performance on graph datasets.
Modelling Convex Shape Priors and Matching Based on the Gromov-Wasserstein Distance.J
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$k$-Nearest Neighbors in Gromov--Wasserstein Space
Proves universal consistency of GW-k-NN on finite-support metric measure spaces with uniform measure and of fGW-k-NN on node-attributed versions, with competitive empirical performance on graph datasets.