Do not get lost in projection: finding the right distance for meaningful UMAP embeddings

Dimensionality reduction techniques are essential for visualizing and analyzing high-dimensional data.This study explores the impact of distance measures on the performance of Uniform Manifold Approximation and Projection (UMAP), a widely used dimensionality reduction method.We evaluate their influence on cluster separation, structure preservation, and their effectiveness when used as a preprocessing step for classification tasks on real and synthetic datasets.The results highlight the importance of tailoring distance measures to specific data contexts and provide guidance for optimizing UMAP applications.