This repository provides an official implementation of SU classification, which is a weakly-supervised classification problem only from pairwise similarity pairs (two data points belong to the same class) and unlabeled data points.
cvxopt==1.1.9
numpy==1.13.3
sklearn==0.18.1
python su_learning.py --loss squared --ns 200 --nu 200 --prior 0.7
mpe.py
is a (slightly-modified) implementation of mixture proportion estimation.
We used the author's implementation available here.
- Bao, H., Niu, G., & Sugiyama, M. Classification from Pairwise Similarity and Unlabeled Data. In Proceedings of International Conference on Machine Learning (ICML), 2018. [arxiv]
- Ramaswamy, H. G., Scott, C., & Tewari, A. Mixture proportion estimation via kernel embedding of distributions. In Proceedings of International Conference on Machine Learning (ICML), pp. 2052–2060, 2016.