Inductive Semi-supervised Learning Through Optimal Transport
- URL: http://arxiv.org/abs/2112.07262v1
- Date: Tue, 14 Dec 2021 09:52:01 GMT
- Title: Inductive Semi-supervised Learning Through Optimal Transport
- Authors: Mourad El Hamri, Youn\`es Bennani, Issam Falih
- Abstract summary: The proposed approach, called Optimal Transport Induction (OTI), extends efficiently an optimal transport based transductive algorithm (OTP) to inductive tasks.
A series of experiments are conducted on several datasets in order to compare the proposed approach with state-of-the-art methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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).
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