Semi-Supervised Classification Using Local and Global Regularization
Abstract
In this paper, we propose a semi-supervised learning (SSL) algorithm based on local and global regularization. In the local regularization part, our algorithm constructs a regularized classifier for each data point using its neighborhood, while the global regularization part adopts a Laplacian regularizer to smooth the data labels predicted by those local classifiers. We show that some existing SSL algorithms can be derived from our framework. Finally we present some experimental results to show the effectiveness of our method.
Cite
Text
Wang et al. "Semi-Supervised Classification Using Local and Global Regularization." AAAI Conference on Artificial Intelligence, 2008.Markdown
[Wang et al. "Semi-Supervised Classification Using Local and Global Regularization." AAAI Conference on Artificial Intelligence, 2008.](https://mlanthology.org/aaai/2008/wang2008aaai-semi/)BibTeX
@inproceedings{wang2008aaai-semi,
title = {{Semi-Supervised Classification Using Local and Global Regularization}},
author = {Wang, Fei and Li, Tao and Wang, Gang and Zhang, Changshui},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2008},
pages = {726-731},
url = {https://mlanthology.org/aaai/2008/wang2008aaai-semi/}
}