Dynamic Generalized Gromov-Wasserstein Optimal Transport
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Computer Science > Machine Learning
Title:Dynamic Generalized Gromov-Wasserstein Optimal Transport
Abstract:Gromov--Wasserstein optimal transport (GW-OT) extends classical optimal transport by introducing structure-aware transport cost. This is particularly relevant for spatial transcriptomics, where dynamical reconstruction should preserve tissue structure in addition to matching expression patterns. While static formulations have been widely used for such structure-aware alignment, a general dynamic formulation for reconstructing continuous trajectories is still missing. We introduce Travelling Pair Dynamical Alignment and Trajectory Estimation (TP-DATE), a theoretical and computational framework to generalize GW-OT dynamically in a simulation-free manner. We formulate a broad class of static and dynamic Quadratic-form OT (QOT) through path actions and prove the static dynamic equivalence. We further develop travelling-pair flow matching, which allows interacting conditional paths and marginalizes their interactions into a single vector field. On synthetic and real spatial transcriptomics data, TP-DATE better preserves spatial structure and improves continuous 3D dynamics reconstruction.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2609.20008 [cs.LG] |
| (or arXiv:2609.20008v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20008
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