# hash-based

> Browse 6 items tagged with hash-based.

## Items

- [DB-LSH — Locality-Sensitive Hashing with Query-based Dynamic Bucketing](https://vectordb.ever.works/items/db-lsh-locality-sensitive-hashing-with-query-based-dynamic-bucketing) — ICDE 2023 and TKDE 2023 papers introducing DB-LSH, a locality-sensitive hashing approach with query-based dynamic bucketing for efficient approximate nearest neighbor search.
- [Locality-Sensitive Indexing for Graph-Based ANNS](https://vectordb.ever.works/items/locality-sensitive-indexing-for-graph-based-anns) — SIGIR 2025 paper proposing a locality-sensitive indexing approach for graph-based approximate nearest neighbor search, combining LSH principles with graph structure for improved search accuracy.
- [LSH-APG — Towards Efficient Index Construction and ANNS in High-Dimensional Spaces](https://vectordb.ever.works/items/lsh-apg-towards-efficient-index-construction-and-anns-in-high-dimensional-spaces) — VLDB 2023 paper proposing LSH-APG, a method combining locality-sensitive hashing with adaptive proximity graphs for efficient index construction and approximate nearest neighbor search in high-dimensional spaces.
- [MP-RW-LSH — Multi-probe LSH for A1-Norm Nearest Neighbor Search](https://vectordb.ever.works/items/mp-rw-lsh-multi-probe-lsh-for-a1-norm-nearest-neighbor-search) — VLDB 2021 paper introducing MP-RW-LSH, an efficient multi-probe locality-sensitive hashing solution for A1-norm (Manhattan distance) approximate nearest neighbor search.
- [PM-LSH — A Fast and Accurate In-memory Framework for High-Dimensional ANNS](https://vectordb.ever.works/items/pm-lsh-a-fast-and-accurate-in-memory-framework-for-high-dimensional-anns) — VLDB 2022 paper introducing PM-LSH, an in-memory locality-sensitive hashing framework for high-dimensional approximate nearest neighbor and closest pair search with strong accuracy guarantees.
- [QALSH — Query-Aware Locality-Sensitive Hashing for ANNS](https://vectordb.ever.works/items/qalsh-query-aware-locality-sensitive-hashing-for-anns) — VLDB 2015 paper introducing QALSH, a query-aware locality-sensitive hashing scheme that improves retrieval accuracy by dynamically adjusting hash functions based on query characteristics.

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