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Geometric organization of olfactory descriptor data in the Poincaré disk
Odor quality is commonly represented using high dimensional descriptor profiles, yet their low dimensional organization remains unclear. We investigated whether a two-dimensional hyperbolic embedding can provide an interpretable representation of this structure. We applied hyperbolic metric multidimensional scaling to two complementary datasets: 480 Sagar rating profiles from three participants rating 160 odorants on 15 continuous descriptors, and 4983 GoodScents--Leffingwell molecules annotated with 138 binary descriptors. The embeddings substantially preserved pairwise descriptor distances,
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- arXiv · AI, language, vision and robotics · 2026-09-09T00:54:34.000Z
First collected: 2026-09-20T19:52:05.078Z. This is not the publication date.