Framework by Wei et al. for high-dimensional ANN search using subspace collision techniques. Offers efficiency and accuracy improvements for vector databases.

Research paper on high-dimensional approximate nearest neighbor search focusing on reliable and efficient distance comparison operations. Published in Proceedings of the ACM on Management of Data, Volume 1, Issue 2 in 2023 by Jianyang Gao and Cheng Long.

A C++ library for approximate nearest neighbor searching in arbitrarily high dimensions, developed by David Mount and Sunil Arya at the University of Maryland. Provides data structures and algorithms for both exact and approximate nearest neighbor searching.

MRPT (Multi-Resolution Proximity Trees) is an open-source library for fast approximate nearest neighbor search in high-dimensional vector spaces, applicable to vector database backends.

DET-LSH is a locality-sensitive hashing scheme that introduces a dynamic encoding tree structure to accelerate approximate nearest neighbor (ANN) search in high-dimensional spaces. While it is a research algorithm rather than a production database, it directly targets the core operation behind vector databases—efficient ANN search over vector embeddings—and is relevant for designing or optimizing vector indexing components within vector database systems.

RaBitQ is an open-source library implementing the "Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search" method, providing vector quantization and compression techniques designed to improve efficiency and accuracy of ANN search engines and vector databases operating in high-dimensional spaces.