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 a margin-based risk function. Traditionally, these risk functions are computed based on a labeled data set. We develop a novel technique for estimating such risks using only unlabeled data and the marginal label distribution. We prove that the proposed risk estimator is consistent on high-dimensional data sets 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 with no labeled data whatsoever.
Cite
Text
Balasubramanian et al. "Unsupervised Supervised Learning II: Margin-Based Classification Without Labels." Journal of Machine Learning Research, 2011.Markdown
[Balasubramanian et al. "Unsupervised Supervised Learning II: Margin-Based Classification Without Labels." Journal of Machine Learning Research, 2011.](https://mlanthology.org/jmlr/2011/balasubramanian2011jmlr-unsupervised/)BibTeX
@article{balasubramanian2011jmlr-unsupervised,
title = {{Unsupervised Supervised Learning II: Margin-Based Classification Without Labels}},
author = {Balasubramanian, Krishnakumar and Donmez, Pinar and Lebanon, Guy},
journal = {Journal of Machine Learning Research},
year = {2011},
pages = {3119-3145},
volume = {12},
url = {https://mlanthology.org/jmlr/2011/balasubramanian2011jmlr-unsupervised/}
}