发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出EMS Coreset算法，通过允许非均匀核心集权重实现熵正则化OT耦合的闭式更新，提升了Sinkhorn Coreset的可扩展性，在保证近似质量的同时大幅缩短了大规模数据集处理的运行时间。
AI 中文摘要
核心集（Coresets）可将大型数据集提炼为小型代表性子集，以实现高效的下游学习。然而，基于最优传输（OT）的选择通常需要对传输计划进行密集计算，这限制了其可扩展性。我们提出一种可扩展的Sinkhorn核心集方法，该方法通过允许非均匀核心集权重，实现了熵正则化OT耦合的闭式更新，生成的质心通过软分配对k-means进行了泛化。我们证明了所选测度的渐近一致性以及对数据扰动的Lipschitz稳定性，提供了准确性和鲁棒性保证。在合成与真实基准测试中，与基于Wasserstein和标准Sinkhorn的核心集选择相比，所提方法在达到相当或更优近似质量的同时，大幅缩短了运行时间，尤其在大规模场景下表现突出。
英文摘要
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.
