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J. Solomon, R. Rustamov, L. Guibas, and A. Butscher, Wasserstein Propagation for Semi-Supervised Learning, Proc. International Conference on Machine Learning (ICML 2014).
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.
Bibtex:
@inproceedings{srgb-wpssl-14,
Author = {Justin Solomon and Raif Rustamov and Guibas Leonidas and Adrian Butscher},
Url = {http://jmlr.org/proceedings/papers/v32/solomon14.pdf},
Title = {Wasserstein Propagation for Semi-Supervised Learning},
Pages = {306--314},
Year = {2014},
Booktitle = {Proceedings of the 31st International Conference on Machine Learning}
}
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