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Hierarchical Subspace Sampling: A Unified Framework for High Dimensional Data Reduction, Selectivity Estimation and Nearest Neighbor Search

Summary: Hierarchical Subspace Sampling unifies data reduction, selectivity estimation, and NN search via locally adaptive subspaces. It reveals subspace structure, scales linearly with size and dimensionality, enabling fast, sampling-based query processing. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3371
Venue
SIGMOD
Year
2002
Pagerank
5.7247716e-05
Overall Rank
5,065 | 64.77%
DOI
-

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Rank Citing Paper Year Venue Pagerank
709 Efficient Similarity Search and Classification via Rank Aggregation 2003 SIGMOD 0.00017768547
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