Hubs in Space¶
Radovanović, M., Nanopoulos, & Ivanović, M. (2010). Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data. Journal of Machine Learning Research, 2487-2531.
Cited by¶
1 citation across 1 artifact.
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Mechanisms¶
- Embedding-Then-Clustering Pipeline
- High-dimensional embedding spaces also suffer hubness — a few vectors sit implausibly close to everything, warping nearest-neighbor structure — so a clustering can look clean while resting on geometry no one intended.
This sourceDefines high-dimensional hubness as the emergence of points that occur in unusually many nearest-neighbor lists, distorting distance-based structure.
- High-dimensional embedding spaces also suffer hubness — a few vectors sit implausibly close to everything, warping nearest-neighbor structure — so a clustering can look clean while resting on geometry no one intended.
Verification¶
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Registry ID ref:452940a40adb · see in the full table