TL;DR
This paper introduces DET-LSH, a novel LSH scheme with a dynamic encoding tree that enhances indexing efficiency and query accuracy for approximate nearest neighbor search in high-dimensional spaces.
Contribution
The paper proposes a new encoding-based tree structure called DE-Tree and a corresponding LSH scheme, DET-LSH, which together improve indexing speed and query accuracy over existing methods.
Findings
Up to 6x faster indexing time compared to state-of-the-art methods.
Up to 2x faster query time with improved accuracy.
Theoretical guarantees on query accuracy for DET-LSH.
Abstract
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…
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