Fred Hohman

Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph

Duen Horng Chau, Donghao Ren, Fred Hohman, Dominik Moritz

While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally. This graph encodes the data manifold in its original high-dimens…Read more