Optimal Transport with Relaxed Marginal Constraints

Journal Article (Journal Article)

Optimal transport (OT) is a principled approach for matching, having achieved success in diverse applications such as tracking and cluster alignment. It is also the core computation problem for solving the Wasserstein metric between probabilistic distributions, which has been increasingly used in machine learning. Despite its popularity, the marginal constraints of OT impose fundamental limitations. For some matching or pattern extraction problems, the framework of OT is not suitable, and post-processing of the OT solution is often unsatisfactory. In this paper, we extend OT by a new optimization formulation called Optimal Transport with Relaxed Marginal Constraints (OT-RMC). Specifically, we relax the marginal constraints by introducing a penalty on the deviation from the constraints. Connections with the standard OT are revealed both theoretically and experimentally. We demonstrate how OT-RMC can easily adapt to various tasks by three highly different applications in image analysis and single-cell data analysis. Quantitative comparisons have been made with OT and another commonly used matching scheme to show the remarkable advantages of OT-RMC.

Full Text

Duke Authors

Cited Authors

  • Li, J; Lin, L

Published Date

  • January 1, 2021

Published In

Volume / Issue

  • 9 /

Start / End Page

  • 58142 - 58160

Electronic International Standard Serial Number (EISSN)

  • 2169-3536

Digital Object Identifier (DOI)

  • 10.1109/ACCESS.2021.3072613

Citation Source

  • Scopus