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).

翻译：在本文中,我们解决了旨在为外样数据获得标签预测的感性半监督学习问题。拟议办法称为最佳运输诱导(OTI),有效地扩展了基于最佳运输的最佳转导算法(OTP),为二进制和多级环境的感测任务。对若干数据集进行了一系列试验,以便将拟议办法与最新方法进行比较。实验证明了我们的方法的有效性。我们公布了我们的代码(代码见https://github.com/MouradElHamri/OTI)。