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It is proved that the Wasserstein distance between a standard normal distribution and linear projections of the data is maximized when the projection recovers an independent component, and the OT-ICA algorithm which finds this projection by gradient-based optimization is proposed.
We study a general class of gradient interface models with Hamiltonian $H=\beta\sum V(\nabla\phi)$, $\beta>0$, assuming essentially that the potential $V$ is even, $V'(s)\ge \alpha s$ on $[0,\infty)$ for some $\alpha>0$, and $-M\leq V''\le C$. We establish a Helffer-Sj\"ostrand representation for these models, and use...
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