# Projective Clustering Product Quantization

arXiv: https://arxiv.org/abs/2112.02179 · HTML: https://mlsanity.com/paper/2112.02179/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Multilingual De-Duplication Strategies: Applying scalable similarity search with monolingual & multilingual embedding models](https://mlsanity.com/paper/2406.13695/) (2024)

## Abstract (arXiv)

This paper suggests the use of projective clustering based product quantization for improving nearest neighbor and max-inner-product vector search (MIPS) algorithms. We provide anisotropic and quantized variants of projective clustering which outperform previous clustering methods used for this problem such as ScaNN. We show that even with comparable running time complexity, in terms of lookup-multiply-adds, projective clustering produces more quantization centers resulting in more accurate dot-product estimates. We provide thorough experimentation to support our claims.