Apple Research: Mining UMAP's Internal kNN Graph to Enhance High-Dimensional Sensemaking
Apple ML Research · rss · 2026-07-30
Apple's Machine Learning Research team proposed a new method to enhance high-dimensional data interpretability by leveraging the k-nearest-neighbor (kNN) graph constructed internally by UMAP.
Traditional workflows typically focus only on the final 2D embedding from UMAP, overlooking the internal kNN graph built before projection. This graph encodes the data manifold in its original high-dimensional space, avoiding the distortion introduced by 2D projections.
The study demonstrates that applying standard graph algorithms to this internal representation significantly improves data sensemaking:
- PageRank: Identifies the most representative data points.
- k-core decomposition: Reveals densely connected regions and core structures within the data.
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