YingfanWang/PaCMAP

Health v2 · maintenance only

Adoption not part of the score

1027 stars · 86 forks observed · 2026-10-04

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PaCMAP is a Python library implementing Pairwise Controlled Manifold Approximation, a dimensionality reduction technique that preserves both local and global structure of high-dimensional data for visualization. It is the official implementation of the JMLR-published method, with related extensions like Parametric PaCMAP and LocalMAP.

Use cases

• visualize high-dimensional embeddings in 2D
• reduce dimensionality of single-cell or embedding datasets
• find a UMAP/t-SNE alternative that preserves global structure
• plot clusters in large datasets
• explore manifold structure of feature vectors

When to choose

• you need 2D/3D visualizations balancing local and global structure
• you want a fast, well-tested Python dimension reduction library
• you are comparing manifold learning methods for research

When to avoid

• you need supervised dimensionality reduction with labels
• you need GPU parametric online-learning DR (use ParamRepulsor instead)
• you need exact linear projections like PCA

Facets

library · maturity stable

machine-learning data-visualization math machine-learning data-visualization data-science python cross-platform dimensionality-reduction manifold-learning embedding umap-alternative visualization

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