A Query-Aware Method for Approximate Range Search in Hamming Space

The range search in Hamming space is to explore the binary vectors whose Hamming distances with a query vector are within a given searching threshold. It arises as the core component of many applications, such as image retrieval, pattern recognition, and machine learning. Existing searching methods in Hamming space require much pre-processing overhead, which are not suitable for processing multiple batches of incoming data in a short time. Moreover, significant pre-processing overhead can be a burden when the number of queries is relatively small. In this paper, we propose a query-aware method for the approximate range search in Hamming space with no pre-process. Specifically, to eliminate the impact of data skewness, we introduce JS-divergence to measure the divergence between data's distribution and query's distribution, and specially design a Query-Aware Dimension Partitioning (QADP) strategy to partition the dimensions into several subspaces according to the scales of given searching thresholds. In the subspaces, the candidates can be efficiently obtained by the basic Pigeonhole Principle and our proposed Anti-Pigeonhole Principle. Furthermore, a sampling strategy is designed to estimate the Hamming distance between the query vector and arbitrary binary vector to obtain the final approximate searching results among the candidates. Experimental results on four real-world datasets illustrate that, in comparison with benchmark methods, our method possesses the superior advantages on searching accuracy and efficiency. The proposed method can increase the searching efficiency up to nearly 16 times with high searching accuracy.