Barycentric Simplicial Hashing: ANN Search at 38 bytes/vector (90% Recall)
Barycentric Simplicial Hashing (BSH) is a new method for approximate nearest neighbor search that utilizes a four-state quantization system. This technique allows for efficient binary indexing while achieving competitive recall rates with significantly lower memory usage compared to existing methods. Empirical results demonstrate that BSH can achieve 84-90% recall using only 34-38 bytes per vector, making it a promising alternative to industry-standard approaches.
- ▪Barycentric Simplicial Hashing is a data-dependent binary indexing method for ANN search.
- ▪The method uses a four-state quantization per triangle to assign binary codes to vectors.
- ▪BSH achieves 84-90% Recall@1 while using only 34-38 bytes per vector, compared to 64 bytes for standard methods.
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Published May 26, 2026 | Version 1 Preprint Open Barycentric Simplicial Hashing for Approximate Nearest Neighbor Search: A Four-State Topological Hash Competitive with Industry-Standard Product Quantization at Half the Memory Authors/Creators Pirolo, Andrés Sebastián Description We present Barycentric Simplicial Hashing (BSH), a data-dependent binary indexing method for approximate nearest neighbor (ANN) search that uses the local triangulation of a vector space as a discrete coordinate system. Given a set of database vectors partitioned into Voronoi cells, we construct a k-NN simplicial complex within each cell and assign each vector a compact binary code by evaluating its barycentric zone relative to every triangle in the complex.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Zenodo.