Methods › General › Dimensionality Reduction › Parametric UMAP
Parametric UMAP
Introduced by Tim Sainburg et al. in Parametric UMAP embeddings for representation and semi-supervised learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Parametric UMAP is a non-parametric graph-based dimensionality reduction algorithm that extends the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets 11 May 2023 · 2 repositories · arXiv:2305.06809
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Opening the black-box of Neighbor Embedding with Hotelling's T2 statistic and Q-residuals 5 Sep 2022 · 1 repository · arXiv:2209.01984
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Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey 25 Aug 2021 · 0 repositories · arXiv:2109.02508
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Parametric UMAP embeddings for representation and semi-supervised learning 27 Sep 2020 · 2 repositories · arXiv:2009.12981Syntology ran 4 of 4 samples · 0 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Dimensionality Reduction | 3 |
| Data Visualization | 2 |
| Embeddings Evaluation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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