Neighbor Embeddings Using Unbalanced Optimal Transport Metrics

Muhammad Rana; Keaton Hamm

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arXiv · 2509.19226

Neighbor Embeddings Using Unbalanced Optimal Transport Metrics

Muhammad Rana·Keaton Hamm

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Abstract

This paper proposes the use of the Hellinger--Kantorovich metric from unbalanced optimal transport (UOT) in a dimensionality reduction and learning (supervised and unsupervised) pipeline. The performance of UOT is compared to that of regular OT and Euclidean-based dimensionality reduction methods on several benchmark datasets including MedMNIST. The experimental results demonstrate that, on average, UOT shows improvement over both Euclidean and OT-based methods as verified by statistical hypothesis tests. In particular, on the MedMNIST datasets, UOT outperforms OT in classification 81\% of the time. For clustering MedMNIST, UOT outperforms OT 83\% of the time and outperforms both other metrics 58\% of the time.

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BibTeX RIS

Muhammad Rana, Keaton Hamm. 2025-09-23. Neighbor Embeddings Using Unbalanced Optimal Transport Metrics. https://arxiv.org/abs/2509.19226

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