Tech · Apple Machine Learning
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph
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Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph.
Key facts
- Authors Duen Horng (Polo) Chau, Donghao Ren, Fred Hohman, Dominik Moritz
- Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph
- 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
Summary
Authors Duen Horng (Polo) 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.