  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).
