Technology

UMAP

UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction algorithm that efficiently projects high-dimensional data to 2D or 3D, preserving both local and global data structure.

UMAP is a high-performance dimensionality reduction technique, grounded in manifold learning and topological data analysis. The algorithm constructs a high-dimensional graph (a fuzzy simplicial complex) representing the data's structure, then optimizes a low-dimensional projection to maintain that structure as closely as possible. Critically, UMAP offers significant advantages over older methods like t-SNE: it is substantially faster and more scalable, projecting the 70,000-point MNIST dataset in under 3 minutes, and excels at preserving the global, not just local, topology of the data. This makes it the gold standard for visualizing complex embeddings, identifying clusters, and serving as a robust preprocessing step in machine learning pipelines, particularly in fields like single-cell biology.