# Efficient Locality Sensitive Hashing

> This work by Jingfan Meng is a comprehensive research thesis on efficient locality-sensitive hashing (LSH), covering algorithmic solutions, core primitives, and applications for approximate nearest neighbor search. It is relevant to vector databases because LSH-based indexing is a foundational technique for scalable similarity search over high-dimensional vectors, informing the design of vector indexes, retrieval engines, and similarity search modules in modern vector database systems.

- **URL:** https://repository.gatech.edu/bitstreams/d4bcf2e3-0d86-44c5-8376-d67639a060b4/download
- **Category:** research-papers-surveys
- **Tags:** Ann, Similarity Search, hashing
- **Updated:** 2025-12-25 16:52
- **Canonical page:** https://vectordb.ever.works/pl/items/efficient-locality-sensitive-hashing

## Details

# Efficient Locality Sensitive Hashing

This work by Jingfan Meng is a comprehensive research thesis on efficient locality-sensitive hashing (LSH), covering algorithmic solutions, core primitives, and applications for approximate nearest neighbor search. It is relevant to vector databases because LSH-based indexing is a foundational technique for scalable similarity search over high-dimensional vectors, informing the design of vector indexes, retrieval engines, and similarity search modules in modern vector database systems.

[https://repository.gatech.edu/bitstreams/d4bcf2e3-0d86-44c5-8376-d67639a060b4/download](https://repository.gatech.edu/bitstreams/d4bcf2e3-0d86-44c5-8376-d67639a060b4/download)

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