Wasserstein Dictionary learning and Wasserstein NMF
Relevant paper: Antoine Rolet, Marco Cuturi and Gabriel Peyré. Fast Dictionary Learning with a Smoothed Wasserstein Loss.. To appear in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 41. Supplementary materials.
Code available on the Github repository: http://arolet.github.io/wasserstein-dictionary-learning
Japanese Optical Character Recognition / Dictionary app, available on iOS and Android:
Here is a matlab wrapper for the c++ code from Nicolas Bonneel available on this page, which is a light adaptation of LEMON to solve the network simplex for bipartite graphs. I recommend using the source code, but you can also use one of the system specific compiled matlab function.
- Corrected a bug that appears for highly degenerated problem that made the optimality check fail (leading to infinite cycling)
- Added an argument that allows to limit the maximum number of simplex pivot (defaults to infinity)
- Coming soon</a>