Projective Clustering Product Quantization

Understand

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.