From 3c256469c435dadbcc93bc0d2a08046cbee79a57 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 11:20:39 +0100 Subject: update doc --- docs/source/index.rst | 33 ++------------------------------- 1 file changed, 2 insertions(+), 31 deletions(-) (limited to 'docs/source/index.rst') diff --git a/docs/source/index.rst b/docs/source/index.rst index adbabb6..acfe766 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,20 +6,6 @@ POT: Python Optimal Transport ============================= - -This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. - -It provides the following solvers: - -* OT solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. -* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. - -Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. - - Contents -------- @@ -30,23 +16,8 @@ Contents all examples - -References ----------- - -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. - -[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300). - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. - -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. - -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. +.. include:: ../readme.rst + :start-line: 5 Indices and tables -- cgit v1.2.3