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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
3841
Venue
SIGMOD
Year
2006
Pagerank
5.3910353e-05
Overall Rank
8,655 | 40.62%
DOI
10.1145/1142473.1142532

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{vu_sigmod06,
        title = {{A Non-Linear Dimensionality-Reduction Technique for Fast Similarity Search in Large Databases}},
        author = {Vu, Khanh and Hua, Kien A. and Cheng, Hao and Lang, Sheau-Dong},
        series = {{SIGMOD} '06},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1142473.1142532},
        url = {https://dl.acm.org/doi/10.1145/1142473.1142532},
        year = {2006}
}

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12,472 Transforming Range Queries To Equivalent Box Queries To Optimize Page Access 2010 VLDB 5.093636e-05
12,572 Constrained Locally Weighted Clustering 2008 VLDB 5.093636e-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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