Dissimilarity in Graph-Based Semi-Supervised Classification
Abstract
Label dissimilarity specifies that a pair of examples probably have different class labels. We present a semi-supervised classification algorithm that learns from dissimilarity and similarity information on labeled and unlabeled data. Our approach uses a novel graphbased encoding of dissimilarity that results in a convex problem, and can handle both binary and multiclass classification. Experiments on several tasks are promising.
Cite
Text
Goldberg et al. "Dissimilarity in Graph-Based Semi-Supervised Classification." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Goldberg et al. "Dissimilarity in Graph-Based Semi-Supervised Classification." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/goldberg2007aistats-dissimilarity/)BibTeX
@inproceedings{goldberg2007aistats-dissimilarity,
title = {{Dissimilarity in Graph-Based Semi-Supervised Classification}},
author = {Goldberg, Andrew B. and Zhu, Xiaojin and Wright, Stephen},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {155-162},
volume = {2},
url = {https://mlanthology.org/aistats/2007/goldberg2007aistats-dissimilarity/}
}