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Query-Sensitive Embeddings

Summary: Embedding-based approximate NN for costly similarity; maps objects to a vector space to speed retrieval. Novel query-sensitive distance metric learned with the embedding adapts to the query, boosting accuracy; tested on handwritten digits and time-series, outperforming prior embeddings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3740
Venue
SIGMOD
Year
2005
Pagerank
6.0444989e-05
Overall Rank
5,909 | 59.46%
DOI
10.1145/1066157.1066238

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{athitsos_sigmod05,
        title = {{Query-Sensitive Embeddings}},
        author = {Athitsos, Vassilis and Hadjieleftheriou, Marios and Kollios, George and Sclaroff, Stan},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066238},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066238},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
2,673 DSH: Data Sensitive Hashing for High-Dimensional k-NN Search 2014 SIGMOD 8.2730112e-05
3,440 Approximate Embedding-Based Subsequence Matching of Time Series 2008 SIGMOD 7.4142918e-05
4,927 Neighbor-Sensitive Hashing 2016 VLDB 6.4426453e-05
6,252 Putting Context into Schema Matching 2006 VLDB 5.941182e-05
6,611 Information Preserving XML Schema Embedding 2005 VLDB 5.8228205e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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