Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search in high-dimensional spaces due to its robust theoretical guarantee on query accuracy. Traditional LSH-based methods mainly focus on improving the efficiency and accuracy of the query phase by designing different query strategies, but pay little attention to improving the efficiency of the indexing phase. They typically fine-tune existing data-oriented partitioning trees to index data points and support their query strategies. However, their strategy to directly partition the multi-dimensional space is time-consuming, and performance degrades as the space dimensionality increases. In this paper, we design an encoding-based tree called Dynamic Encoding Tree (DE-Tree) to improve the indexing efficiency and support efficient range queries based on Euclidean distance. Based on DE-Tree, we propose a novel LSH scheme called DET-LSH. DET-LSH adopts a novel query strategy, which performs range queries in multiple independent index DE-Trees to reduce the probability of missing exact NN points, thereby improving the query accuracy. Our theoretical studies show that DET-LSH enjoys probabilistic guarantees on query accuracy. Extensive experiments on real-world datasets demonstrate the superiority of DET-LSH over the state-of-the-art LSH-based methods on both efficiency and accuracy. While achieving better query accuracy than competitors, DET-LSH achieves up to 6x speedup in indexing time and 2x speedup in query time over the state-of-the-art LSH-based methods. This paper was published in PVLDB 2024.

翻译：局部敏感哈希（LSH）因其对查询准确性的鲁棒理论保证，是高维空间中近似最近邻（ANN）搜索的知名解决方案。传统的基于LSH的方法主要通过设计不同的查询策略来提升查询阶段的效率和准确性，但很少关注索引阶段效率的改进。它们通常微调现有的面向数据的分区树来索引数据点并支持其查询策略。然而，它们直接划分多维空间的策略耗时严重，且性能随空间维度增加而下降。本文设计了一种称为动态编码树（DE-Tree）的基于编码的树结构，以提高索引效率并支持基于欧氏距离的高效范围查询。基于DE-Tree，我们提出了一种新颖的LSH方案，称为DET-LSH。DET-LSH采用了一种新颖的查询策略，通过在多个独立的索引DE-Tree中执行范围查询来降低遗漏精确最近邻点的概率，从而提升查询准确性。我们的理论研究表明，DET-LSH在查询准确性上具有概率保证。在真实数据集上的大量实验表明，DET-LSH在效率和准确性上均优于最先进的基于LSH的方法。在取得比竞争对手更好查询准确性的同时，DET-LSH在索引时间上相比最先进的基于LSH的方法最高可加速6倍，在查询时间上最高可加速2倍。本文发表于PVLDB 2024。