Abstract
We propose a new general framework for recovering low-rank structure in optimal transport using Schatten-p norm regularization. Our approach extends existing methods that promote sparse and interpretable transport maps or plans, while providing a unified and principled family of convex programs that encourage low-dimensional structure. The convexity of our formulation enables direct theoretical analysis: we derive optimality conditions and prove recovery guarantees for low-rank couplings, barycentric displacements, and cross-covariances in simplified settings. To efficiently solve the proposed program, we develop a mirror descent algorithm with convergence guarantees in the convex setting. Experiments on synthetic and real data demonstrate the method's efficiency, scalability, and ability to recover low-rank transport structures. In particular, we demonstrate its utility on a machine-learning task in learning transport between high-dimensional cell perturbations for biological applications. All code is publicly available at https://github.com/twmaunu/schatten_ot.