Inductive Semi-supervised Learning Through Optimal Transport
This work addresses the problem of making label predictions for new data in semi-supervised learning, which is incremental as it extends an existing method to inductive settings.
The paper tackles the inductive semi-supervised learning problem for out-of-sample data by proposing Optimal Transport Induction (OTI), which extends an optimal transport-based transductive method to inductive tasks, and experiments show its effectiveness compared to state-of-the-art methods.
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).