Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search
Summary: Proposes QALSH, a query-aware LSH for c-ANN in external memory that uses the query as an anchor to partition buckets, removing the need for a random shift. It supports any c>1, provides theoretical query guarantees, and empirically outperforms C2LSH and LSB-Forest, especially in high dimensions with c<2. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qiang Huang
- 2. Jianlin Feng
- 3. Yikai Zhang
- 4. Qiong Fang
- 5. Wilfred Ng
Incoming Citations (Sorted by Pagerank)
Showing 14 of 64 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 34 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00076637636 |
| 605 | Locality-Sensitive Hashing Scheme Based on Dynamic Collision Counting | 2012 | SIGMOD | 0.000193396 |
| 709 | Efficient Similarity Search and Classification via Rank Aggregation | 2003 | SIGMOD | 0.00017768547 |
| 867 | SRS: Solving c-Approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index | 2015 | VLDB | 0.00015792021 |
| 1,229 | SK-LSH : An Efficient Index Structure for Approximate Nearest Neighbor Search | 2014 | VLDB | 0.00013157271 |
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