Abstract
Dimensionality reduction algorithms, such as UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-distributed Stochastic Neighbor Embedding), are widely used to simplify complex datasets by reducing the number of variables while preserving essential information as much as possible. This allows for various tasks, such as data visualization or training machine learning models.H…