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 effectiveness of our approach. We make our code publicly available (Code is available at: https://github.com/MouradElHamri/OTI).
@article{arxiv.2112.07262,
title = {Inductive Semi-supervised Learning Through Optimal Transport},
author = {Mourad El Hamri and Younès Bennani and Issam Falih},
journal= {arXiv preprint arXiv:2112.07262},
year = {2021}
}