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arXiv · 2110.01446

Label Propagation Through Optimal Transport

Mourad El Hamri·Younès Bennani·Issam Falih
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Abstract

In this paper, we tackle the transductive semi-supervised learning problem that aims to obtain label predictions for the given unlabeled data points according to Vapnik's principle. Our proposed approach is based on optimal transport, a mathematical theory that has been successfully used to address various machine learning problems, and is starting to attract renewed interest in semi-supervised learning community. The proposed approach, Optimal Transport Propagation (OTP), performs in an incremental process, label propagation through the edges of a complete bipartite edge-weighted graph, whose affinity matrix is constructed from the optimal transport plan between empirical measures defined on labeled and unlabeled data. OTP ensures a high degree of predictions certitude by controlling the propagation process using a certainty score based on Shannon's entropy. We also provide a convergence analysis of our algorithm. Experiments task show the superiority of the proposed approach over the state-of-the-art. We make our code publicly available.

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Mourad El Hamri, Younès Bennani, Issam Falih. 2021-10-01. Label Propagation Through Optimal Transport. https://doi.org/10.1109/ijcnn52387.2021.9533521

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