arXiv:2608. 16506v1 Announce Type: cross Abstract: Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets.
By Keyi Li, Yuval Kluger, Boris Landa
arXiv:2607. 06646v1 Announce Type: cross Abstract: This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and structural connectivity through optimal transport.
By Iman Seyedi, Francesco Archetti
arXiv:2609.26748v1 Announce Type: cross
Abstract: Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters,...
By Siyi Wang, Alexandre Leblanc, Paul D. McNicholas
Existing optimal transport (OT) models primarily seek an OT map or plan between distributions by minimizing a prescribed transport cost or distortion. However, minimizing transport cost or distortion alone may fail to identify a geometrically meaningful transformation between the two distributions.
The paper introduces CluProp, a framework that treats varied‑density clustering in high‑dimensional spaces as a label propagation process over neighborhood graphs. By combining density‑based ideas with graph connectivity, it offers a deterministic propagation strategy that reduces parameter sensitivity and scales efficiently to millions of points. CluProp is agnostic to distance metrics and consistently outperforms existing baselines in accuracy while processing large datasets in minutes.
By Yingtao Zheng, Hugo Phibbs, Ninh Pham
arXiv:2606. 02047v1 Announce Type: cross Abstract: We introduce Convex Distance Operator Transport (CDOT), the first convex optimal transport framework that aligns distributions across heterogeneous domains by jointly preserving feature correspondence and intrinsic geometric structure.
By Junhyoung Chung, Euijong Song, Won Hwa Kim, Gunwoong Park