Semi-Supervised Mean Fields
Abstract
A novel semi-supervised learning approach based on statistical physics is proposed in this paper. We treat each data point as an Ising spin and the interaction between pairwise spins is captured by the similarity between the pairwise points. The labels of the data points are treated as the directions of the corresponding spins. In semi-supervised setting, some of the spins have fixed directions (which corresponds to the labeled data), and our task is to determine the directions of other spins. An approach based on the Mean Field theory is proposed to achieve this goal. Finally the experimental results on both toy and real world data sets are provided to show the effectiveness of our method.
Cite
Text
Wang et al. "Semi-Supervised Mean Fields." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Wang et al. "Semi-Supervised Mean Fields." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/wang2007aistats-semisupervised/)BibTeX
@inproceedings{wang2007aistats-semisupervised,
title = {{Semi-Supervised Mean Fields}},
author = {Wang, Fei and Wang, Shijun and Zhang, Changshui and Winther, Ole},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {596-603},
volume = {2},
url = {https://mlanthology.org/aistats/2007/wang2007aistats-semisupervised/}
}