Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
This work addresses classification accuracy for researchers and practitioners using semi-supervised learning, but it appears incremental as it extends existing statistical equivalences.
The authors tackled the problem of improving classification accuracy in semi-supervised learning by developing a logistic learning method based on exponential tilt mixture models, which demonstrated advantages over existing methods in numerical results.
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.