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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)

Paper ID
1246
Venue
PODS
Year
2001
Pagerank
5.1105081e-05
Overall Rank
6,325 | 56.00%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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