{"ID":2867272,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19226","arxiv_id":"2509.19226","title":"Neighbor Embeddings Using Unbalanced Optimal Transport Metrics","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.","short_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 d...","url_abs":"https://arxiv.org/abs/2509.19226","url_pdf":"https://arxiv.org/pdf/2509.19226v1","authors":"[\"Muhammad Rana\",\"Keaton Hamm\"]","published":"2025-09-23T16:49:15Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[]","has_code":false}