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Implementation for "Classification from Pairwise Similarity and Unlabeled Data"
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LICENSE Create LICENSE Jun 4, 2018
README.md
image.png
misc.py
mpe.py
su_learning.py

README.md

Classification from Pairwise Similarity and Unlabeled Data

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.

image

Dependencies

cvxopt==1.1.9
numpy==1.13.3
sklearn==0.18.1

Run

python su_learning.py --loss squared --ns 200 --nu 200 --prior 0.7

Notes

mpe.py is a (slightly-modified) implementation of mixture proportion estimation. We used the author's implementation available here.

References

  • 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.
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