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