Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
Abstract
In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption. We adopt a Bayesian perspective and demonstrate that, for a suitable choice of prior constructed with sufficiently many unlabeled data, the posterior contracts around the truth at a rate that is minimax optimal up to a logarithmic factor. Our theory covers both regression and classification.
Cite
Text
Sanz-Alonso and Yang. "Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective." Journal of Machine Learning Research, 2022.Markdown
[Sanz-Alonso and Yang. "Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective." Journal of Machine Learning Research, 2022.](https://mlanthology.org/jmlr/2022/sanzalonso2022jmlr-unlabeled/)BibTeX
@article{sanzalonso2022jmlr-unlabeled,
title = {{Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective}},
author = {Sanz-Alonso, Daniel and Yang, Ruiyi},
journal = {Journal of Machine Learning Research},
year = {2022},
pages = {1-28},
volume = {23},
url = {https://mlanthology.org/jmlr/2022/sanzalonso2022jmlr-unlabeled/}
}