Fast approximate similarity search based on degree-reduced neighborhood graphs

By Kazuo Aoyama, Kazumi Saito, Hiroshi Sawada, Naonori Ueda. 2011

44 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Retrieval & Search, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jingdong Wang
2. Dmitry A. Yashunin
3. Gang Zeng
4. Yury Malkov
5. Shipeng Li
6. Baining Guo
7. Yi Yang
8. Ying Zhang

In Information Retrieval & Search

1. Dmitry A. Yashunin
2. Yury Malkov
3. Jingdong Wang
4. Ying Zhang
5. Gang Zeng
6. Yifang Sun
7. Shipeng Li
8. W. Zhang

In Signal Processing

1. Dmitry A. Yashunin
2. Yury Malkov
3. Jingdong Wang
4. Ying Zhang
5. Shipeng Li
6. W. Zhang
7. Qiang Yue
8. Yuxiang Wang

Citations in the corpus

• Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, background): “Proximity graph K-ANNS algorithms [10], [18], [19], [20], [21], [22], [23], [24], [25],”
• Fast Neighborhood Graph Search Using Cartesian Concatenation (2013, background): “Recently neighborhood graph search has been attracting a lot of interests [1, 2, 5, 15, 31, 32, 38] because of its simplicity and good search performance.”
• A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search (2021, methodology): “Interestingly, the NGT-like path adjustment and range search are also used by the k-DR algorithm in [7] (see Appendix N for details).”
• Approximate Nearest Neighbor Search on High Dimensional Data - Experiments, Analyses, and Improvement (v1.0) (2016, background): “Since the exact computation of K-NN graph is costly, many heuristics have been proposed in the literature [16, 5].”