[1]LI X,ZHANG W,DING Q. Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction[J]. Reliability engineering & system safety,2019,182(C):208-218.
[2]WU H,LIU Y,WANG J. Review of text classification methods on deep learning[J]. Computers,materials & continua,2020,63(3):1309-1321.
[3]PURWINS H,LI B,VIRTANEN T,et al. Deep learning for audio signal processing[J]. IEEE journal of selected topics in signal processing,2019,13(2):206-219.
[4]LITJENS G,KOOI T,BEJNORDI B E,et al. A survey on deep learning in medical image analysis[J]. Medical image analysis,2017,42:60-88.
[5]DA'U A,SALIM N. Recommendation system based on deep learning methods:a systematic review and new directions[J]. Artificial intelligence review,2020,53(4):2709-2748.
[6]ZHENG B,ZHAO X,WENG L. et al. PM-LSH:a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search[J]. The VLDB journal,2022,30(6):1339-1363.
[7]BABENKO A,LEMPITSKY V. The inverted multi-Index[J]. IEEE transactions on pattern analysis and machine intelligence,2015,37(6):1247-1260.
[8]GE T Z,HE K M,KE Q F,et al. Optimized product quantization[J]. IEEE transactions on pattern analysis and machine intelligence,2014,36(4):744-755.
[9]KALANTIDIS Y,AVRITHIS Y. Locally optimized product quantization for approximate nearest neighbor search[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus:IEEE,2014:2321-2328.
[10]FU C,XIANG C,WANG C,et al. Fast approximate nearest neighbor search with the navigating spreading-out graph[J]. Proceedings of the VLDB endowment,2019,12(5):461-474.
[11]SUBRAMANYA J S,DEVVRIT F,SIMHADRI H V,et al. DiskANN:fast accurate billion-point nearest neighbor search on a single node[C]//Neural Information Processing Systems. Canada:IEEE,2019,32.
[12]MALKOV Y A,YASHUNIN D A. Efficient and robust approximate near-est neighbor search using hierarchical navigable small world graphs[J]. CoRR,2016.
[13]BARANCHUK D,BABENKO A,MALKOV Y. Revisiting the inverted indices for billion-scale approximate nearest neighbors[C]//Proceedings of the European Conference on Computer Vision(ECCV). Munich:Springer-Verlag,2018:202-216.
[14]MOROUZI M B A,FLEET D J. Cartesian k-means[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland:IEEE,2013:3017-3024.
[15]WANG J,WANG J,SONG J,et al. Optimized cartesian k-means[J]. IEEE transactions on knowledge and data engineering,2014,27(1):180-192.
[16]LINDE Y,BUZO A,GRAY R. An algorithm for vector quantizer design[J]. IEEE transactions on communications,1980,28(1):84-95.
[17]ZHAN J,MAO J,LIU Y,et al. Jointly optimizing query encoder and product quantization to improve retrieval performance[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Santonni:ACM,2021:2487-2496.
[18]BENTLEY J L. Multidimensional binary search trees used for associative searching[J]. Communications of the ACM,1975,18(9):509-517.
[19]CIACCIA P,PATELLA M,ZEZULA P. M-tree:an Efficient access method for similarity search in metric spaces[C]//Proceedings of the 23rd VLDB Conference. Athens,Greece:ACM,1997:426-435.
[20]GUTTMAN A. R-trees:A dynamic index structure for spatial searching[C]//Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data. Boston:ACM,1984:47-57.
[21]ANDONI A,INDYK P,NGUYEN H L,et al. Beyond locality-sensitive hashing[C]//Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics. PA:ACM,2014:1018-1028.
[22]DATAR M,IMMORLICA N,INDYK P,et al. Locality-sensitive hashing scheme based on p-stable distributions[C]//Proceedings of the Twentieth Annual Symposium on Computational Geometry. New York:ACM,2004:253-262.
[23]PAULEVE L,JEGOU H,AMSALEG L. Locality sensitive hashing:a comparison of hash functiontypes and querying mechanisms[J]. Pattern recognition letters,2010,31(11):1348-1358.
[24]HARWOOD B,DRUMMOND T. Fanng:fast approximate nearest neighbour graphs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE,2016:5713-5722.
[25]JAROMCZYK J W,TOUSSAINT G T. Relative neighborhood graphs and their relatives[J]. Proceedings of the IEEE,1992,80(9):1502-1517.
[26]TOUSSAINT G T. The relative neighbourhood graph of a finite planar set[J]. Pattern recognition,1980,12(4):261-268.
[27]AURENHAMM F. Voronoi diagrams—a survey of a fundamental geometric data structure[J]. ACM computing surveys(CSUR),1991,23(3):345-405.
[28]HAJEBI K,ABBASI-YADKORI Y,SHAHBAZI H,et al. Fast approximate nearest-neighbor search with k-nearest neighbor graph[C]//Twenty-Second International Joint Conference on Artificial Intelligence. Catalonsa,Spain:ACM,2011.
[29]LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision,2004,60(2):91-110.
[30]DOUZE M,JÉGOU H,Sandhawalia H,et al. Evaluation of gist descriptors for webscale image search[C]//Proceedings of the ACM International Conference on Image and Video Retrieval. Santorini,Greece:ACM,2009:1-8.