EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coreset
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 competi...
| Authors | Haoyun Yin, Chuanhui Liu, Xiao Wang |
|---|---|
| Primary category | stat.ML |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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