Similarity search on neighbor's graphs with automatic Pareto optimal performance and minimum expected quality setups based on hyperparameter optimization
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Abstract (arXiv)
This manuscript introduces an autotuned algorithm for searching nearest neighbors based on neighbor graphs and optimization metaheuristics to produce Pareto-optimal searches for quality and search speed automatically; the same strategy is also used to produce indexes that achieve a minimum quality. Our approach is described and benchmarked with other state-of-the-art similarity search methods, showing convenience and competitiveness.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 19 Jan 2022.
Builds on
5 of 28 references.
References · 5
- 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
- 2015Practical and Optimal LSH for Angular Distancecited by 105
- 2017Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graphcited by 103
- 2018Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Datacited by 20
- 2017A scalable solution to the nearest neighbor search problem through local-search methods on neighbor graphscited by 1
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Citing papers per year: 2023: 1.
1 paper.