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Approximate nearest neighbors: towards removing the curse of dimensionality
Abstract
Article Free Access Share on Approximate nearest neighbors: towards removing the curse of dimensionality Authors: Piotr Indyk Department of Computer Science, Stanford University, Stanford, CA Department of Computer Science, Stanford University, Stanford, CAView Profile , Rajeev Motwani Department of Computer Science, Stanford University, Stanford, CA Department of Computer Science, Stanford University, Stanford, CAView Profile Authors Info & Claims STOC '98: Proceedings of the thirtieth annual ACM symposium on Theory of computingMay 1998 Pages 604–613https://doi.org/10.1145/276698.276876Online:23 May 1998Publication History 2,363citation11,545DownloadsMetricsTotal Citations2,363Total Downloads11,545Last 12 Months769Last 6 weeks97 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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In the citing paper
“However, SSTH suffers from serious efficiency issue due to the involvement of de-binarization and de-LSTM.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“Given six query videos of different topics, the proposed algorithm makes fewer mistakes in retrieving the similar videos for each query compared with SSTH.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“SSTH [29] / / 8.21h / / 8.23h / / 8.48h / / 8.55h”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“For example, the precision@100 at 64 bits reaches 26.1% under the proposed framework and the difference over SSTH (20.4%) is 5.7%.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“For examples, the mAP@5 values at the code length of 128 are 59.3% and 57.1% for UDVH-TSN and UDVH-LSTM on FCVID, which are at least 7% higher than that in SSTH (50.1%).”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“Moreover, the UDVH-based methods substantially yield better retrieval performance over SSTH, which is consistent with the results reported in Fig. 5 and further demonstrates the superiority of our self-taught training strategy involving clustering and balanced rotation.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“Considering the deep architectures usually converge within t = 5 loops for the UDVH-based methods, the total training cost is less than 4.5 hours, which is much shorter than training SSTH (over 8 hours), thus indicating the high efficiency of the proposed hashing framework.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“The most time-consuming parts of SSTH include the backpropagation of Binary LSTM units when updating their binary outputs and the complex network structures [29], which are not adopted in our algorithm.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
In the citing paper
“Specifically, as shown in Fig. 9(a), the mAP@5 values at 128 bits on FCVID are 50.1% and 57.1% for SSTH and UDVH-LSTM, which are over 15% higher than the values achieved when using CNN features.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [29]
Citations
Cites
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,621
Rayward-Smith, V. J., Cormen, T. H., Leiserson, C. E., & Rivest, R. L. (1991) · Journal of the Operational Research Society · doi:10.2307/2583667 · Cited by 16,958
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Introduction to Modern Information Retrieval
Salton, G., & McGill, M. J. (1983) · Virtual Defense Library (Ministerio de Defensa) · Cited by 9,743
Analysis of a complex of statistical variables into principal components.
Hotelling, H. (1933) · Journal of Educational Psychology · doi:10.1037/h0071325 · Cited by 9,691
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,498
Vector Quantization and Signal Compression
Gersho, A., & Gray, R. M. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3626-0 · Cited by 7,055
Motwani, R., & Raghavan, P. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814075 · Cited by 5,098
Self-citationPattern classification and scene analysis
Chien, Y. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100577 · Cited by 3,421
Cited by
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Monga, V., Banerjee, A., & Evans, B. L. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2005.863502 · Cited by 121
Order preserving hashing for approximate nearest neighbor search
Wang, J., Wang, J., Yu, N., & Li, S. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502100 · Cited by 119
Zhou, H.-Y., Wang, F., & Tao, P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00652 · Cited by 119
Designing sensitive viral diagnostics with machine learning
Metsky, H. C., Welch, N. L., Pillai, P. P., Haradhvala, N. J., Rumker, L., Mantena, S., Zhang, Y. B., Yang, D. K., Ackerman, C. M., Weller, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01213-5 · Cited by 123
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Boutsidis, C., Zouzias, A., & Petros Drineas, B. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1011.4632 · Cited by 119
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Levin, K., Henry, K. E., Jansen, A., & Livescu, K. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707765 · Cited by 118
Wang, J., Wang, J., Song, J., Xu, X.-S., Shen, H. T., & Li, S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2324592 · Cited by 118
Finding interesting associations without support pruning
Cohen, E., Datar, M., Fujiwara, S., Gionis, A., Indyk, P., Motwani, R., Ullman, J. D., & Yang, C. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2000.839448 · Cited by 118
Self-citationThe power of comparative reasoning
Yagnik, J., Strelow, D., Ross, D. A., & Lin, R.-S. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126527 · Cited by 117
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Fuller, B., Varia, M., Yerukhimovich, A., Shen, E., Hamlin, A., Gadepally, V., Shay, R., Mitchell, J., & Cunningham, R. (2017) · IEEE Symposium on Security and Privacy · doi:10.1109/sp.2017.10 · Cited by 125
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Panigrahy, R. (2006) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1145/1109557.1109688 · Cited by 117
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Blum, A. L. (2006) · Lecture notes in computer science · doi:10.1007/11752790_3 · Cited by 114
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Sundaram, N., Turmukhametova, A., Satish, N., Mostak, T., Indyk, P., Madden, S. R., & Dubey, P. K. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2556549.2556574 · Cited by 113
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ÖZSU, M. T. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-016-5554-y · Cited by 111
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Boytsov, L., & Naidan, B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41062-8_28 · Cited by 111
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Qamra, A., Meng, Y., & Chang, E. Y. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.54 · Cited by 111
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Kenthapadi, K., Korolova, A., Mironov, I., & Mishra, N. (2013) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v5i1.625 · Cited by 131
Jain, H., Balasubramanian, V., Chunduri, B. R., & Varma, M. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290979 · Cited by 110
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Tang, B., & He, H. (2015) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2015.7256954 · Cited by 114
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Heo, J., Lee, Y., He, J., Chang, S., & Yoon, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408363 · Cited by 110
DPPro: Differentially Private High-Dimensional Data Release via Random Projection
Xu, C., Ren, J., Zhang, Y., Qin, Z., & Ren, K. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737966 · Cited by 112
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Chaudhuri, S., & Koltun, V. (2010) · ACM Transactions on Graphics · doi:10.1145/1882261.1866205 · Cited by 109
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He, J., Chang, S., Radhakrishnan, R., & Bauer, C. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995518 · Cited by 108
A descriptor for large scale image retrieval based on sketched feature lines
Eitz, M., Hildebrand, K., Boubekeur, T., & Alexa, M. (2009) · Sketch Based Interfaces and Modeling · doi:10.1145/1572741.1572747 · Cited by 108
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Sadeghi, M. A., & Forsyth, D. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_5 · Cited by 107
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Qian, J., Li, X., Zhang, C., Chen, L., Jung, T., & Han, J. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2697854 · Cited by 111
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Bogolin, S.-V., Croitoru, I., Jin, H., Liu, Y., & Albanie, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00513 · Cited by 118
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Fu, C., & Cai, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.07228 · Cited by 107
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Matei, B., Shan, Y., Sawhney, H., Tan, Y., Kumar, R., Huber, D., & Hebert, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.148 · Cited by 106
A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds
Landrieu, L., Raguet, H., Vallet, B., Mallet, C., & Martin Weinmann, A. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.08.010 · Cited by 106
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Wang, H., Yao, M., Jiang, G., Mi, Z., & Fu, X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3239033 · Cited by 106
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Andoni, A., Indyk, P., Ên, H. L. N., & Razenshteyn, I. (2013) · Cited by 105
Self-citationInteractive search in image retrieval: a survey
Thomée, B., & Lew, M. S. (2012) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-012-0014-4 · Cited by 105
Learning Multi-View Neighborhood Preserving Projections
Quadrianto, N., & Lampert, C. H. (2011) · Cited by 105
An Adaptive Nearest Neighbor Classification Algorithm for Data Streams
Law, Y.-N., & Zaniolo, C. (2005) · Lecture notes in computer science · doi:10.1007/11564126_15 · Cited by 105
Image similarity search with compact data structures
Lv, Q., Charikar, M., & Li, K. (2004) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1031171.1031213 · Cited by 105
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Koga, H., 石橋, 徹., & Watanabe, T. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0027-5 · Cited by 105
Lower bounds for high dimensional nearest neighbor search and related problems
Borodin, A., Ostrovsky, R., & Rabani, Y. (1999) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/301250.301330 · Cited by 105
A Survey on Locality Sensitive Hashing Algorithms and their ApplicationsPreprint
Jafari, O., Maurya, P., Nagarkar, P., Islam, K. M., & Crushev, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08942 · Cited by 105
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Aggarwal, C. C., & Yu, P. S. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.991713 · Cited by 102
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Zhou, K., & Hongyuan Zha, B. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339611 · Cited by 101
Building models of animals from video
Ramanan, D., Forsyth, D. A., & Barnard, K. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.155 · Cited by 101
Li, P., Owen, A. B., & Zhang, C. (2013) · Cited by 101
Gaze guided object recognition using a head-mounted eye tracker
Toyama, T., Kieninger, T., Faisal Shafait, B., & Dengel, A. R. (2012) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/2168556.2168570 · Cited by 101
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Peleg, D. (1999) · Lecture notes in computer science · doi:10.1007/3-540-46784-x_5 · Cited by 101
Liu, Y., Cui, J., Huang, Z. H., Li, H., & Shen, H. T. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2732939.2732947 · Cited by 100
Zheng, B., Zhao, X., Weng, L., Nguyen, Q. V. H., Liu, H., & Jensen, C. S. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3377369.3377374 · Cited by 100
Arora, A., Sinha, S., Kumar, P., & Bhattacharya, A. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3204028.3204034 · Cited by 109
A Fast Search Algorithm for a Large Fuzzy Database
Hao, F., Daugman, J. G., & Zieliński, P. (2008) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2008.920726 · Cited by 99
Scalable and Sustainable Deep Learning via Randomized Hashing
Spring, R., & Shrivastava, A. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098035 · Cited by 98
Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces
Dong, W., Charikar, M., & Li, K. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390358 · Cited by 98
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Hoe, J. T., Ng, K. W., Zhang, T., Chan, C. S., Song, Y.-Z., & Xiang, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14449 · Cited by 98
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Bergamo, A., Torresani, L., & Fitzgibbon, A. W. (2011) · Cited by 97
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Zhao, X., Tian, Y., Huang, K., Zheng, B., & Zhou, X. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3594512.3594527 · Cited by 97
Density-based indexing for approximate nearest-neighbor queries
Bennett, K. P., Fayyad, U. M., & Geiger, D. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312236 · Cited by 97
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George Kollios, D., Gunopulos, D., & Tsotras, V. J. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48344-6_7 · Cited by 97
Zhang, X., Du, S., & Wang, Q. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.05.006 · Cited by 96
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Andoni, A., Indyk, P., & Pǎtraşcu, M. (2006) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2006.56 · Cited by 95
Self-citationUsing paraphrases for improving first story detection in news and Twitter
Petrović, S., Osborne, M., & Lavrenko, V. P. (2012) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 95
Manzoor, E., Lamba, H., & Akoglu, L. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220107 · Cited by 95
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection
Qian, J., ElSherief, M., Belding, E., & Wang, W. Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2019 · Cited by 101
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Kong, L., Wang, L., Gong, W., Yan, C., Duan, Y., & Qi, L. (2021) · World Wide Web · doi:10.1007/s11280-021-00941-z · Cited by 95
Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets
Houle, M. E., & Sakuma, J. (2005) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2005.66 · Cited by 95
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Dai, B., Guo, R., Kumar, S., He, N., & Song, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.02815 · Cited by 96
Text classification by augmenting bag of words (BOW) representation with co-occurrence feature
Department of Computer Science and Engineering, G. E. C., George K, S., & Joseph, S. (2014) · IOSR Journal of Computer Engineering · doi:10.9790/0661-16153438 · Cited by 94
Locally Linear Hashing for Extracting Non-linear Manifolds
Irie, G., Li, Z., Wu, X.-M., & Chang, S. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.272 · Cited by 94
Multiple feature kernel hashing for large-scale visual search
Liu, X., He, J., & Lang, B. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.08.022 · Cited by 94
Approximate NN Queries on Streams with Guaranteed Error/performance Bounds
Koudas, N., Ooi, B. C., Tan, K., & Zhang, R. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50071-1 · Cited by 94
Spatial selection for attentional visual tracking
Yang, M., Yuan, J., & Wu, Y. N. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383178 · Cited by 93
Probabilistic Polynomials and Hamming Nearest Neighbors
Alman, J., & Williams, R. (2015) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2015.18 · Cited by 93
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Zhang, Y., Huang, K., Geng, G., & Liu, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40991-2_42 · Cited by 93
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Gorisse, D., Cord, M., & Precioso, F. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.193 · Cited by 92
Miller, R. J. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3229863.3240491 · Cited by 92
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Ram, P., & Gray, A. G. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339677 · Cited by 92
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Emam, M., Han, Q., & Niu, X. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2872-2 · Cited by 92
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Leibe, B., Mikolajczyk, K., & Schiele, B. (2006) · British Machine Vision Conference (BMVC) · doi:10.5244/c.20.81 · Cited by 92
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Metadata
- Authors
- Indyk, P., & Motwani, R.
- Affiliations
- Stanford University (US)
- ORCID
- Indyk, Piotr
- Venue
- Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98
- Publisher
- ACM Press
- Published
- 1998
- Pages
- 604-613
- OpenAlex
- W2147717514
- Cited by
- 4,188
- References
- 76
- Citations by year
- 2026: 152 · 2025: 162 · 2024: 164 · 2023: 133 · 2022: 160 · 2021: 223 · 2020: 245 · 2019: 250 · 2018: 249 · 2017: 251 · 2016: 277 · 2015: 280 · 2014: 260 · 2013: 224 · 2012: 208
- FWCI
- 23.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Symposium on Theory of Computing (STOC) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computational Geometry and Mesh Generation
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Keywords
- approximate nearest neighbor search, curse of dimensionality, nearest neighbor search, metric spaces, metric embeddings, randomized algorithms
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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