Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density
Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offers a computationally tractable framework for this task by encoding cross-dataset affinities in a transport plan. However, when two datasets are sampled from geometrically similar low-dimensional structures with substantially different sampling densities, the EOT plan may match points by relative sampling density rather than geometric proximity, yielding geometrically misleading correspondences. To address this issue, we propose a density-reweighted EOT framework in which the influence of sampling density on the ...
| Authors | Keyi Li, Yuval Kluger, Boris Landa |
|---|---|
| Primary category | stat.ML |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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