Optimal Transport: From Theory to Tweaks, Computations and Applications in Machine Learning

References

Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller. "Nonlinear component analysis as a kernel eigenvalue problem." Neural computation10.5 (1998): 1299-1319.

Tenenbaum, Joshua B., Vin De Silva, and John C. Langford. "A global geometric framework for nonlinear dimensionality reduction." science290.5500 (2000): 2319-2323.

Roweis, Sam T., and Lawrence K. Saul. "Nonlinear dimensionality reduction by locally linear embedding." Science 290.5500 (2000): 2323-2326.

Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of Machine Learning Research 9.Nov (2008): 2579-2605.

Davis, Jason V., et al. "Information-theoretic metric learning." Proceedings of the 24th international conference on Machine learning. ACM, 2007.

Weinberger, Kilian Q., and Lawrence K. Saul. "Distance metric learning for large margin nearest neighbor classification." Journal of Machine Learning Research 10.Feb (2009): 207-244.

Aurélien Bellet, Amaury Habrard, Marc Sebban: Metric Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2015

Kulis, Brian. "Metric learning: A survey." Foundations and Trends in Machine Learning 5.4 (2012): 287-364.

Cuturi, Marco, et al. "A kernel for time series based on global alignments."2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07. Vol. 2. IEEE, 2007.

Villani, Cédric. Optimal transport: old and new. Vol. 338. Springer Science & Business Media, 2008.

Mémoli, Facundo. "Gromov–Wasserstein distances and the metric approach to object matching." Foundations of computational mathematics 11.4 (2011): 417-487.