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Adaptable Similarity Search using Non-Relevant Information

Summary: Robust adaptive similarity search that incorporates non-relevant feedback to prune the candidate space. Hyperplane decision surface, normal to the min-distance from non-relevant points to the convex hull of relevant points, splits space into relevant/non-relevant regions to guide ranking; validated on simulated and benchmark data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h7319e8fe39f16b43
Venue
VLDB
Year
2002
Pagerank
5.3501479e-05
Overall Rank
8,349 | 43.87%
DOI
10.1016/B978-155860869-6/50013-5

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{tv_vldb02,
        title = {{Adaptable Similarity Search using Non-Relevant Information}},
        author = {T.V., Ashwin and Gupta, Rahul and Ghosal, Sugata},
        journal = {PVLDB},
        series = {{VLDB} '02},
        doi = {10.1016/B978-155860869-6/50013-5},
        url = {https://doi.org/10.1016/B978-155860869-6/50013-5},
        year = {2002}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,337 Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval 2003 SIGMOD 5.1911928e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

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