Quantization to speedup approximate nearest neighbor search

Abstract

AbstractThe quantization-based approaches not only are the effective methods for solving the problems of approximate nearest neighbor search, but also effectively reduce storage space. However, many quantization-based approaches usually employ fixed nprobes to the search process for each query. This will lead to extra query consumption. Additionally, we observed that as the number of points in each cluster center of product quantization increases, the query cost also increases. To address this issue, we propose an acceleration strategy based on the IVF-HNSW framework to further speed up the query process. This strategy involves introducing an adaptive termination condition for queries and reducing the number of data points accessed by building HNSW results. Through extensive experiments, we have shown that our proposed method significantly accelerates the nearest neighbor search process.