UMAP

UMAP is a dimensionality reduction technique that is particularly effective for visualizing high-dimensional data. It works by approximating the manifold structure of the data, preserving both local and global data structures. UMAP is widely used in various fields, including data science and machine learning, for tasks such as clustering, visualization, and feature extraction. Its efficiency and scalability make it suitable for large datasets, and it often outperforms other dimensionality reduction methods like t-SNE in preserving the overall data topology.