On the Effects of Dimensionality Reduction on High Dimensional Similarity Search
Summary: Provides an intuitive model and diagnostic method explaining when and why dimensionality reduction helps or harms high‑dimensional similarity search, showing effects are highly data‑dependent. Argues information‑loss minimization maximizes precision/recall but not qualitative optimality, and small implementation tweaks to reduction methods can substantially improve similarity search quality. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,024 | ATLAS: A Probabilistic Algorithm for High Dimensional Similarity Search | 2011 | SIGMOD | 9.7519678e-05 |
| 12,277 | Transforming Range Queries To Equivalent Box Queries To Optimize Page Access | 2010 | VLDB | 4.1945683e-05 |
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