Unsupervised Supervised Learning II: Margin-Based Classification Without Labels
Abstract
Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing margin-based risk functions. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and knowledge of $p(y)$. We prove that the proposed risk estimator is consistent on high-dimensional datasets and demonstrate it on synthetic and real-world data. In particular, we show how the estimate is used for evaluating classifiers in transfer learning, and for training classifiers using exclusively unlabeled data.
Cite
Text
Balasubramanian et al. "Unsupervised Supervised Learning II: Margin-Based Classification Without Labels." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Balasubramanian et al. "Unsupervised Supervised Learning II: Margin-Based Classification Without Labels." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/balasubramanian2011aistats-unsupervised/)BibTeX
@inproceedings{balasubramanian2011aistats-unsupervised,
title = {{Unsupervised Supervised Learning II: Margin-Based Classification Without Labels}},
author = {Balasubramanian, Krishnakumar and Donmez, Pinar and Lebanon, Guy},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {137-145},
volume = {15},
url = {https://mlanthology.org/aistats/2011/balasubramanian2011aistats-unsupervised/}
}