Smooth and Sparse Optimal Transport
This work addresses the need for interpretable and sparse transportation plans in applications like color transfer, representing an incremental improvement over existing entropic regularization methods.
The paper tackles the problem of dense transportation plans in optimal transport by introducing a strongly convex regularization framework that yields sparse and group-sparse plans, with theoretical bounds showing that squared 2-norm regularization can achieve smaller approximation errors than entropic regularization in some cases.
Entropic regularization is quickly emerging as a new standard in optimal transport (OT). It enables to cast the OT computation as a differentiable and unconstrained convex optimization problem, which can be efficiently solved using the Sinkhorn algorithm. However, entropy keeps the transportation plan strictly positive and therefore completely dense, unlike unregularized OT. This lack of sparsity can be problematic in applications where the transportation plan itself is of interest. In this paper, we explore regularizing the primal and dual OT formulations with a strongly convex term, which corresponds to relaxing the dual and primal constraints with smooth approximations. We show how to incorporate squared $2$-norm and group lasso regularizations within that framework, leading to sparse and group-sparse transportation plans. On the theoretical side, we bound the approximation error introduced by regularizing the primal and dual formulations. Our results suggest that, for the regularized primal, the approximation error can often be smaller with squared $2$-norm than with entropic regularization. We showcase our proposed framework on the task of color transfer.