  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.
