## Distributional Sliced Embedding Discrepancy for Incomparable Distributions

**M. Z. Alaya, M. Bérar, G. Gasso, A. Rakotomamonjy**

*arXiv*, 2021

Gromov-Wasserstein (GW) distance is a key tool for manifold learning and cross- domain learning, allowing the comparison of distributions that do not live in the same metric space. Because of its high computational complexity, several approximate GW distances have been proposed based on entropy regularization or on slicing, and one-dimensional GW computation. **Read more**