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A Non-Linear Dimensionality-Reduction Technique for Fast Similarity Search in Large Databases

Summary: Proposes a non-linear dimensionality-reduction scheme that extracts two parameters to bound the search volume around the query sphere, independent of dimensionality. Uses a workspace-mapping mechanism to derive tight bounds and enable distance lower-bounding for fast, index-based similarity search with empirical gains over state of the art. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3779
Venue
SIGMOD
Year
2006
Pagerank
4.4768766e-05
Overall Rank
8,647 | 39.85%
DOI
-

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Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,654 Moirae: History-Enhanced Monitoring 2007 CIDR 4.9733876e-05
12,277 Transforming Range Queries To Equivalent Box Queries To Optimize Page Access 2010 VLDB 4.1945683e-05
12,379 Constrained Locally Weighted Clustering 2008 VLDB 4.1945683e-05
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