Inductive Semi-supervised Learning Through Optimal Transport

@inproceedings{Hamri2021InductiveSL,
  title={Inductive Semi-supervised Learning Through Optimal Transport},
  author={Mourad El Hamri and Youn{\`e}s Bennani and Issam Falih},
  booktitle={ICONIP},
  year={2021}
}
In this paper, we tackle the inductive semi-supervised learning problem that aims to obtain label predictions for out-of-sample data. The proposed approach, called Optimal Transport Induction (OTI), extends efficiently an optimal transport based transductive algorithm (OTP) to inductive tasks for both binary and multi-class settings. A series of experiments are conducted on several datasets in order to compare the proposed approach with state-of-the-art methods. Experiments demonstrate the… 
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Incremental Unsupervised Domain Adaptation Through Optimal Transport

The proposed approach, called DA-OTP, aims to learn a gradual subspace alignment of the source and target domains through Supervised Locality Preserving Projection, so that projected data in the joint low-dimensional latent subspace can be domain-invariant and easily separable.

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