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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
1245
Venue
PODS
Year
2001
Pagerank
5.8148791e-05
Overall Rank
6,646 | 54.41%
DOI
10.1145/375551.383213

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aggarwal_pods01,
        address = {New York, NY, USA},
        series = {{PODS} '01},
        title = {{On the Effects of Dimensionality Reduction on High Dimensional Similarity Search}},
        url = {https://dl.acm.org/doi/10.1145/375551.383213},
        doi = {10.1145/375551.383213},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Aggarwal, Charu C.},
        year = {2001}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,935 ATLAS: A Probabilistic Algorithm for High Dimensional Similarity Search 2011 SIGMOD 9.4560124e-05
12,472 Transforming Range Queries To Equivalent Box Queries To Optimize Page Access 2010 VLDB 5.093636e-05
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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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