# Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models

arXiv: https://arxiv.org/abs/1906.07882 · HTML: https://mlsanity.com/paper/1906.07882/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [On semi-supervised estimation using exponential tilt mixture models](https://mlsanity.com/paper/2311.08504/) (2023)

## Abstract (arXiv)

Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training. The goal is to exploit both datasets to achieve higher prediction accuracy than just using labeled data alone. We develop a semi-supervised logistic learning method based on exponential tilt mixture models, by extending a statistical equivalence between logistic regression and exponential tilt modeling. We study maximum nonparametric likelihood estimation and derive novel objective functions which are shown to be Fisher consistent. We also propose regularized estimation and construct simple and highly interpretable EM algorithms. Finally, we present numerical results which demonstrate the advantage of the proposed methods compared with existing methods.