UMAP-Learn

Overview

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction algorithm for visualization and general non-linear dimensionality reduction. It is faster than t-SNE, scales to larger datasets, preserves both local and global structure, and supports supervised learning and embedding of new data points.

When to Use

• Reducing high-dimensional data to 2D/3D for visualization
• Preprocessing for density-based clustering (HDBSCAN, DBSCAN)
• Feature engineering in ML pipelines (transform new data into learned embedding)
• Supervised/semi-supervised embedding with partial labels
• Tracking embeddings across time points or batches (AlignedUMAP)
• Density-preserving embeddings (DensMAP)
• Neural network-based embedding with custom architectures (Parametric UMAP)
• For linear dimensionality reduction use PCA (scikit-learn)
• For neighborhood-graph construction without embedding use scikit-learn NearestNeighbors

Prerequisites