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宋子文 等: 向量数据库中近似最近邻搜索关键技术综述                                                      1001


                      SIAM Symp. on Discrete Algorithms. Austin: Society for Industrial and Applied Mathematics, 1993. 311–321.
                 [57]   Tavallali P, Tavallali P, Singhal M. K-means tree: An optimal clustering tree for unsupervised learning. The Journal of Supercomputing,
                      2021, 77(5): 5239–5266. [doi: 10.1007/s11227-020-03436-2]
                 [58]   Fukunaga K, Narendra PM. A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. on Computers, 1975, C-
                      24(7): 750–753. [doi: 10.1109/T-C.1975.224297]
                 [59]   spotify/annoy. 2025. https://github.com/spotify/annoy
                 [60]   FLANN—Fast library for approximate nearest neighbors. 2025. https://github.com/flann-lib/flann
                 [61]   Ma HZ, Li JZ, Zhang Y. Reconsidering tree based methods for k-maximum inner-product search: The LRUS-covertree. In: Proc. of the
                      40th IEEE Int’l Conf. on Data Engineering. Utrecht: IEEE, 2024. 4671–4684. [doi: 10.1109/ICDE60146.2024.00355]
                 [62]   Voronoi diagram. 2025. https://en.wikipedia.org/wiki/Voronoi_diagram
                 [63]   Milvus. 2025. https://milvus.io/zh
                 [64]   Xu Q, Yang J, Zhang F, Pan JD, Chen K, Shen YR, Zhou AC, Du XY. Tribase: A vector data query engine for reliable and lossless
                      pruning compression using triangle inequalities. Proc. of the ACM on Management of Data, 2025, 3(1): 82. [doi: 10.1145/3709743]
                 [65]   Wei  JQ,  Lee  X,  Liao  ZY,  Palpanas  T,  Peng  BT.  Subspace  collision:  An  efficient  and  accurate  framework  for  high-dimensional
                      approximate nearest neighbor search. Proc. of the ACM on Management of Data, 2025, 3(1): 79. [doi: 10.1145/3709729]
                 [66]   Bruch S, Nardini FM, Rulli C, Venturini R. Efficient inverted indexes for approximate retrieval over learned sparse representations. In:
                      Proc. of the 47th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Washington: ACM, 2024. 152–162.
                      [doi: 10.1145/3626772.3657769]
                 [67]   Xu YM, Liang HY, Li J, Xu ST, Chen Q, Zhang QX, Li C, Yang ZY, Yang F, Yang YQ, Cheng P, Yang M. SPFresh: Incremental in-
                      place  update  for  billion-scale  vector  search.  In:  Proc.  of  the  29th  Symp.  on  Operating  Systems  Principles.  Koblenz:  ACM,  2023.
                      545–561. [doi: 10.1145/3600006.3613166]
                 [68]   Jafari  O,  Maurya  P,  Nagarkar  P,  Islam  KM,  Crushev  C.  A  survey  on  locality  sensitive  hashing  algorithms  and  their  applications.
                       arXiv:2102.08942, 2021.
                 [69]   Datar M, Immorlica N, Indyk P, Mirrokni VS. Locality-sensitive hashing scheme based on p-stable distributions. In: Proc. of the 20th
                      Annual Symp. on Computational Geometry. New York: ACM, 2004. 253–262. [doi: 10.1145/997817.997857]
                 [70]   Gan JH, Feng JL, Fang Q, Ng W. Locality-sensitive hashing scheme based on dynamic collision counting. In: Proc. of the 2012 ACM
                      SIGMOD Int’l Conf. on Management of Data. Scottsdale: ACM, 2012. 541–552. [doi: 10.1145/2213836.2213898]
                 [71]   Zhao  X,  Chen  ZH,  Huang  K,  Zhang  RY,  Zheng  BL,  Zhou  XF.  Efficient  approximate  maximum  inner  product  search  over  sparse
                      vectors. In: Proc. of the 40th IEEE Int’l Conf. on Data Engineering. Utrecht: IEEE, 2024. 3961–3974. [doi: 10.1109/ICDE60146.2024.
                      00303]
                 [72]   Zhao X, Zheng BL, Yi XM, Luan XF, Xie C, Zhou XF, Jensen CS. FARGO: Fast maximum inner product search via global multi-
                      probing. Proc. of the VLDB Endowment, 2023, 16(5): 1100–1112. [doi: 10.14778/3579075.3579084]
                 [73]   Norouzi  M,  Punjani  A,  Fleet  DJ.  Fast  search  in  Hamming  space  with  multi-index  hashing.  In:  Proc.  of  the  2012  IEEE  Conf.  on
                      Computer Vision and Pattern Recognition. Providence: IEEE, 2012. 3108–3115. [doi: 10.1109/CVPR.2012.6248043]
                 [74]   Qin JB, Xiao C, Wang YS, Wang W, Lin XM, Ishikawa Y, Wang GR. Generalizing the pigeonhole principle for similarity search in
                      Hamming space. IEEE Trans. on Knowledge and Data Engineering, 2021, 33(2): 489–505. [doi: 10.1109/TKDE.2019.2899597]
                 [75]   Liu QY, Shen YY, Chen L. HAP: An efficient Hamming space index based on augmented pigeonhole principle. In: Proc. of the 2022 Int’l
                      Conf. on Management of Data. Philadelphia: ACM, 2022. 917–930. [doi: 10.1145/3514221.3517880]
                 [76]   Ge TZ, He KM, Ke QF, Sun J. Optimized product quantization. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2014, 36(4):
                      744–755. [doi: 10.1109/TPAMI.2013.240]
                 [77]   Norouzi M, Fleet DJ. Cartesian K-means. In: Proc. of the 2013 IEEE Conf. on Computer Vision and Pattern Recognition. Portland:
                      IEEE, 2013. 3017–3024. [doi: 10.1109/CVPR.2013.388]
                 [78]   Babenko A, Lempitsky V. Additive quantization for extreme vector compression. In: Proc. of the 2014 IEEE Conf. on Computer Vision
                      and Pattern Recognition. Columbus: IEEE, 2014. 931–938. [doi: 10.1109/CVPR.2014.124]
                 [79]   Martinez J, Clement J, Hoos HH, Little JJ. Revisiting additive quantization. In: Proc. of the 14th European Conf. on Computer Vision.
                      Amsterdam: Springer, 2016. 137–153. [doi: 10.1007/978-3-319-46475-6_9]
                 [80]   Zhang T, Du C, Wang JD. Composite quantization for approximate nearest neighbor search. In: Proc. of the 31st Int’l Conf. on Machine
                      Learning. 2014. II-838–II-846.
                 [81]   Wang  JF,  Wang  JD,  Song  JK,  Xu  XS,  Shen  HT,  Li  SP.  Optimized  cartesian  K-means.  IEEE  Trans.  on  Knowledge  and  Data
                      Engineering, 2015, 27(1): 180–192. [doi: 10.1109/TKDE.2014.2324592]
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