Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification

DOI10.1080/10618600.2023.2165500arXiv2205.13573MaRDI QIDQ6141173

No author found.

Publication date: 22 January 2024

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2205.13573

zbMATH Keywords

importance samplingelement-wise samplingSinkhorn-scaling algorithmunbalanced Gromov-Wasserstein distance

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Gromov-Wasserstein distances and the metric approach to object matching
• A homogenized model for vortex sheets
• An interpolating distance between optimal transport and Fisher-Rao metrics
• Unbalanced optimal transport: dynamic and Kantorovich formulations
• Sampled Gromov Wasserstein
• Sliced and Radon Wasserstein barycenters of measures
• Monte Carlo strategies in scientific computing.
• On the geometry of metric measure spaces. I
• Concerning nonnegative matrices and doubly stochastic matrices
• Optimal Transport in Competition with Reaction: The Hellinger--Kantorovich Distance and Geodesic Curves
• Scaling algorithms for unbalanced optimal transport problems
• The Monge–Kantorovitch mass transfer and its computational fluid mechanics formulation
• The Gromov–Wasserstein distance between networks and stable network invariants
• Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data
• Optimal Transport

This page was built for publication: Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification