Distributionally robust k-means minimizes worst-case squared distance over a Wasserstein-2 ball around the empirical distribution, yielding a tractable soft-clustering algorithm with monotonic block coordinate descent and local linear convergence.
A distributionally robust approach to Shannon limits using the Wasserstein distance
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Distributionally Robust K-Means Clustering
Distributionally robust k-means minimizes worst-case squared distance over a Wasserstein-2 ball around the empirical distribution, yielding a tractable soft-clustering algorithm with monotonic block coordinate descent and local linear convergence.