EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coreset

Abstract:Coresets distill large datasets into small, representative subsets for efficient downstream learning. Yet Optimal Transport (OT)-based selection typically requires intensive computation of transport plans, limiting scalability. We introduce a scalable Sinkhorn coreset method that permits closed-form updates of the entropically regularized OT coupling by allowing non-uniform coreset weights. This produces centroids that generalize k-means via soft assignments. We establish asymptotic consistency of the selected measure and Lipschitz stability to data perturbations, providing accuracy and robustness guarantees. Across synthetic and real-world benchmarks, the proposed method achieves competitive or improved approximation quality while substantially reducing runtime compared to Wasserstein- and standard Sinkhorn-based coreset selection, especially at large scale.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2608.16101 [stat.ML]

(or arXiv:2608.16101v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2608.16101 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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