Abstract
Approximate k nearest neighbor (AkNN) search is a primitive operator for many applications, such as computer vision and machine learning. As these applications deal with a large set of high-dimensional points, a fast and accurate solution is required. It is known that graph-based AkNN search algorithms are faster and more accurate than other approaches, including hash- and quantization-based ones. However, existing graph-based AkNN search algorithms rely purely on heuristics, i.e., their performances are not theoretically supported. This paper proposes LGTM, a new algorithm for AkNN search, that exploits both locality-sensitive hashing and a proximity graph. The performance of LGTM is theoretically supported. Our experiments on real datasets show that LGTM outperforms state-of-the-art AkNN search algorithms.
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Notes
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In our implementation, we maintain at most 50 points in each bucket.
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NSG and HNSW can process different queries in parallel, but they cannot process a query in parallel.
References
Babenko, A., Lempitsky, V.: Aggregating local deep features for image retrieval. In: ICCV, pp. 1269–1277 (2015)
Babenko, A., Lempitsky, V.: Efficient indexing of billion-scale datasets of deep descriptors. In: CVPR, pp. 2055–2063 (2016)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: SoCG, pp. 253–262 (2004)
Dong, W., Moses, C., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: WWW, pp. 577–586 (2011)
Fu, C., Xiang, C., Wang, C., Cai, D.: Fast approximate nearest neighbor search with the navigating spreading-out graph. PVLDB 12(5), 461–474 (2019)
Harwood, B., Drummond, T.: FANNG: fast approximate nearest neighbour graphs. In: CVPR, pp. 5713–5722 (2016)
Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2010)
Li, W., et al.: Approximate nearest neighbor search on high dimensional data-experiments, analyses, and improvement. IEEE Trans. Knowl. Data Eng. 32(8), 1475–1488 (2020)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Malkov, Y.A., Yashunin, D.: Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 824–836 (2020)
Weber, R., Schek, H.J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: VLDB, pp. 194–205 (1998)
Acknowledgments
This research is partially supported by JST PRESTO Grant Number JPMJPR1931, JSPS Grant-in-Aid for Scientific Research (A) Grant Number 18H04095, and JST CREST Grant Number JPMJCR21F2.
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Arai, Y., Amagata, D., Fujita, S., Hara, T. (2021). LGTM: A Fast and Accurate kNN Search Algorithm in High-Dimensional Spaces. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12924. Springer, Cham. https://doi.org/10.1007/978-3-030-86475-0_22
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