How does the choice of distance metric affect dimensionality reduction?

Distance metrics determine how similarity is measured, affecting embeddings significantly.

Dimensionality reduction methods like t-SNE, UMAP, and Isomap rely on distance or similarity measures. Euclidean distance may fail in high dimensions due to concentration effects, while cosine or Manhattan distances may better capture relationships depending on data type. The chosen metric directly shapes neighborhood structure and final embedding quality.