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About this paper
Zhiyuan Hua, Qiji Mo, Zebin Yao, Lixiao Cui, Xiaoguang Liu, Gang Wang, Zijing Wei, Xinyu Liu and 3 more
Introducing Escape Hardness, a framework dynamically enhances Approximate Nearest Neighbor Search (ANNS) graphs by detecting and fixing structural defects, notably improving search speed by 1.3 to 6.88 times and reducing distance calculations for out-of-distribution queries compared to prior methods. The approach achieves this with significantly fewer historical queries and robust index maintenance capabilities for dynamic datasets.
Key insights
- The "Escape Hardness" (EH) metric accurately quantifies graph quality around queries, showing a strong negative correlation with search accuracy and enabling adaptive edge additions.
- Effective graph repair involves a two-stage approach: improving initial reachability to the query's vicinity (RFix) and enhancing local connectivity within that neighborhood (NGFix).
- Utilizing a significantly smaller set of historical queries with approximate kNN computations can still yield superior performance for ANNS, reducing construction overhead and reliance on extensive historical data.
Summary by alphaXiv.
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