Feature Robust Optimal Transport for High-dimensional Data

Understand

Optimal transport is a machine learning problem with applications including distribution comparison, feature selection, and generative adversarial networks.

• In this paper, we propose feature-robust optimal transport (FROT) for high-dimensional data, which solves high-dimensional OT problems using feature selection to avoid the curse of dimensionality.
• Specifically, we find a transport plan with discriminative features.
• To this end, we formulate the FROT problem as a min--max optimization problem.