Integral Probability Metric based Regularization for Optimal Transport

10 Nov 2020  ·  Piyushi Manupriya, J. Saketha Nath, Pratik Jawanpuria ·

Regularization in Optimal Transport (OT) problems has been shown to critically affect the associated computational and sample complexities. It also has been observed that regularization effectively helps in handling noisy marginals as well as marginals with unequal masses... However, existing works on OT restrict themselves to $\phi$-divergences based regularization. In this work, we propose and analyze Integral Probability Metric (IPM) based regularization in OT problems. While it is expected that the well-established advantages of IPMs are inherited by the IPM-regularized OT variants, we interestingly observe that some useful aspects of $\phi$-regularization are preserved. For example, we show that the OT formulation, where the marginal constraints are relaxed using IPM-regularization, also lifts the ground metric to that over (perhaps un-normalized) measures. Infact, the lifted metric turns out to be another IPM whose generating set is the intersection of that of the IPM employed for regularization and the set of 1-Lipschitz functions under the ground metric. Also, in the special case where the regularization is squared maximum mean discrepancy based, the proposed OT variant, as well as the corresponding Barycenter formulation, turn out to be those of minimizing a convex quadratic subject to non-negativity/simplex constraints and hence can be solved efficiently. Simulations confirm that the optimal transport plans/maps obtained with IPM-regularization are intrinsically different from those obtained with $\phi$-regularization. Empirical results illustrate the efficacy of the proposed IPM-regularized OT formulation. This draft contains the main paper and the Appendices. read more

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