Replaces traditional dimensionality reduction techniques with a manifold learning algorithm based on Riemannian geometry. Optimized for preserving both local and global structure in massive datasets.
Tech Stack
PythonMathematics & Statistics
Why umap?
• Superior speed to t-SNE
• Preserves global topology
• Strong mathematical foundation
Limitations
• Stochastic output
• Sensitive hyperparameters
• High memory for graph construction
3/5/2026
Last Update
860
Forks
523
Issues
BSD-3-Clause
License
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