Summary of Probabilistic Routing For Graph-based Approximate Nearest Neighbor Search, by Kejing Lu et al.
Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
by Kejing Lu, Chuan Xiao, Yoshiharu Ishikawa
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Databases (cs.DB); Data Structures and Algorithms (cs.DS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is introduced in this paper that enhances routing within graph-based approximate nearest neighbor search (ANNS) methods, offering a probabilistic guarantee for exploring a node’s neighbors. The method, called PEOs, efficiently identifies which neighbors should be considered for exact distance calculation, leading to significant improvements in efficiency. The authors formulate the problem as probabilistic routing and develop two baseline strategies using locality-sensitive techniques. Experiments show that PEOs outperforms leading-edge routing techniques by 1.1-1.4 times, increasing throughput on graph indexes HNSW and NSSG by a factor of 1.6-2.5. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of finding the closest point to a given point in high-dimensional spaces. It introduces a new way to do this called PEOs, which is faster and better than other methods. The authors tried different approaches and found that PEOs works best. They tested it on some common datasets and it was 1.6-2.5 times faster than the current fastest method. |
Keywords
* Artificial intelligence * Nearest neighbor
