Wasserstein Propagation for Semi-Supervised Learning
Abstract
Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semi-supervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application; in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.
Cite
Text
Solomon et al. "Wasserstein Propagation for Semi-Supervised Learning." International Conference on Machine Learning, 2014.Markdown
[Solomon et al. "Wasserstein Propagation for Semi-Supervised Learning." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/solomon2014icml-wasserstein/)BibTeX
@inproceedings{solomon2014icml-wasserstein,
title = {{Wasserstein Propagation for Semi-Supervised Learning}},
author = {Solomon, Justin and Rustamov, Raif and Guibas, Leonidas and Butscher, Adrian},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {306-314},
volume = {32},
url = {https://mlanthology.org/icml/2014/solomon2014icml-wasserstein/}
}