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Optimal transport with Laplacian regularization

Abstract : We propose a method based on optimal transport for empirical distributions with Laplacian regularization (LOT). Laplacian regularization is a graph-based regu-larization that can encode neighborhood similarity between samples either on the final position of the transported samples or on their displacement. In both cases, LOT is expressed as a quadratic programming problem and can be solved with a Frank-Wolfe algorithm with optimal step size. Result on domain adaptation and a shape matching problems show the interest of using this regularization in optimal transport.
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Submitted on : Wednesday, January 14, 2015 - 9:00:14 AM
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  • HAL Id : hal-01103076, version 1


Rémi Flamary, Nicolas Courty, Alain Rakotomamonjy, Devis Tuia. Optimal transport with Laplacian regularization. NIPS 2014, Workshop on Optimal Transport and Machine Learning, Dec 2014, Montréal, Canada. ⟨hal-01103076⟩



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