Inductive Semi-supervised Learning Through Optimal Transport

TL;DR

This paper introduces Optimal Transport Induction (OTI), a novel method for inductive semi-supervised learning that extends optimal transport techniques to predict labels for new, unseen data efficiently.

Contribution

The paper presents a new inductive semi-supervised learning approach based on optimal transport, extending a transductive algorithm to handle out-of-sample data effectively.

Findings

OTI outperforms state-of-the-art methods in experiments

Effective for both binary and multi-class problems

Code is publicly available for reproducibility

Abstract

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).