UMAP (Uniform Manifold Approximation and Projection)

UMAP (Uniform Manifold Approximation and Projection) is a technique used to reduce complex, high-dimensional data into a lower-dimensional space (like 2D or 3D) for easier visualization and analysis. It works by assuming that the data points lie on a curved, multi-dimensional surface (manifold) and seeks to preserve the data’s structure and relationships as closely as possible during the reduction. UMAP is efficient and effective at revealing patterns, groupings, or clusters in data, making it useful in fields like machine learning, bioinformatics, and data visualization.