Barycentric Weak Inner-Product Gromov-Wasserstein

• URL: http://arxiv.org/abs/2608.25145v1
• Date: Tue, 25 Aug 2026 20:53:18 GMT
• Title: Barycentric Weak Inner-Product Gromov-Wasserstein
• Abstract summary: Gromov-Wasserstein (GW) compares distributions through relations within each space.<n>We introduce a weak GW framework that compares source relations with relations between the target conditional laws induced by a coupling.<n>We show that wIGW searches for an intermediate target geometry that can be refined into the prescribed target law without changing conditional means.
• Score: 10.617854230082896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gromov-Wasserstein (GW) compares distributions through relations within each space. This pointwise comparison can be too sensitive in one-to-many settings, where several target outcomes refine one source state and their mean carries the geometry of interest. We introduce a weak GW framework that compares source relations with relations between the target conditional laws induced by a coupling. For inner-product relations, we retain the conditional means $m_π(x)=\mathbb{E}_π[Y\mid X=x]$. The resulting barycentric weak inner-product GW (wIGW) satisfies $\mathrm{wIGW}_{\mathrm{bar}}^2(μ,ν)=\inf_{η\preceq_{\mathrm{cx}}ν}\mathrm{IGW}^2(μ,η)$. Here $η\preceq_{\mathrm{cx}}ν$ means that $ν$ is a mean-preserving spread of $η$. Thus wIGW searches for an intermediate target geometry that can be refined into the prescribed target law without changing conditional means. Under finite second moments, minimizers exist and martingale gluing recovers an optimal coupling. With ridge regularization, moment duality gives an $A$-$B$ min-max problem whose inner step is weak optimal transport with a quadratic cost parameterized by $A$ and $B$; the outer problem optimizes these matrices. For finitely supported measures, we give an iterative algorithm. Under a quantitative ridge condition, the reduced problem is convex--concave, and the projected outer iteration satisfies an explicit contraction bound for inexact inner solves. Point cloud and graph feature refinement experiments illustrate how mean-preserving target refinements can have zero cost. A paired peripheral blood mononuclear cell (PBMC) multiome study evaluates atlas based cell type transfer through RNA/ATAC alignment in cell to cell and prototype to cell settings, with the prototype to cell setting representing the one-to-many case.

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