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Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
Summary: Local Dimensionality Reduction (LDR) discovers locally correlated clusters and reduces dimensionality within each cluster, avoiding global PCA's distance loss on non-global data. A cluster-aware index supports point, range, and kNN queries with lower cost than GDR or scans.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hd7fca381eedc04ec
Venue
VLDB
Year
2000
Pagerank
0.00010013999
Overall Rank
1,634 | 89.02%
DOI
-
Incoming Non-self Citations Over Time
BibTeX Citation
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@article{chakrabarti_vldb00,
title = {{Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces}},
author = {Chakrabarti, Kaushik and Mehrotra, Sharad},
journal = {PVLDB},
series = {{VLDB} '00},
year = {2000}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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