# Label Propagation Through Optimal Transport

arXiv: https://arxiv.org/abs/2110.01446 · HTML: https://mlsanity.com/paper/2110.01446/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Joint Distribution Optimal Transportation for Domain Adaptation](https://mlsanity.com/paper/1705.08848/) (2017)
- [Theoretical Analysis of Domain Adaptation with Optimal Transport](https://mlsanity.com/paper/1610.04420/) (2016)
- [Optimal Transport for Multi-source Domain Adaptation under Target Shift](https://mlsanity.com/paper/1803.04899/) (2018)
- [Co-clustering through Optimal Transport](https://mlsanity.com/paper/1705.06189/) (2017)
- [Interactive Image Segmentation using Label Propagation through Complex Networks](https://mlsanity.com/paper/1901.02573/) (2019)

## Built on by (most cited first, 4 of 4)

- [Confident Sinkhorn Allocation for Pseudo-Labeling](https://mlsanity.com/paper/2206.05880/) (2022)
- [Inductive Semi-supervised Learning Through Optimal Transport](https://mlsanity.com/paper/2112.07262/) (2021)
- [Beyond Mapping : Domain-Invariant Representations via Spectral Embedding of Optimal Transport Plans](https://mlsanity.com/paper/2601.13350/) (2026)
- [Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer](https://mlsanity.com/paper/2610.14853/) (2026)

## Abstract (arXiv)

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.