Product Quantization with K-means++ Initialization for Embedded Vector Search
Year: 2026
Type: article
Abstract: We present an optimized implementation of Product Quantization (PQ) for high-dimensional vector similarity search, incorporating K-means++ initialization for improved codebook quality and Asymmetric Distance Computation (ADC) for efficient search. Our implementation achieves 8-16x memory compression with 95-98% search accuracy (recall@10) while enabling 100-1000x search speedup through distance table precomputation. The system is designed for embedded and resource-constrained environments, making billion-scale vector search practical on edge devices and mobile platforms.
Author: Moya Vaca, Daniel
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7141643534
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