Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search

February 07, 2024 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Yutaro Oguri, Yusuke Matsui arXiv ID 2402.04713 Category cs.IR: Information Retrieval Cross-listed cs.DB, cs.LG Citations 1 Venue arXiv.org Last Checked 4 months ago
Abstract
We present a theoretical and empirical analysis of the adaptive entry point selection for graph-based approximate nearest neighbor search (ANNS). We introduce novel concepts: $b\textit{-monotonic path}$ and $B\textit{-MSNET}$, which better capture an actual graph in practical algorithms than existing concepts like MSNET. We prove that adaptive entry point selection offers better performance upper bound than the fixed central entry point under more general conditions than previous work. Empirically, we validate the method's effectiveness in accuracy, speed, and memory usage across various datasets, especially in challenging scenarios with out-of-distribution data and hard instances. Our comprehensive study provides deeper insights into optimizing entry points for graph-based ANNS for real-world high-dimensional data applications.
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