The DOI matches its Crossref record; 74 of the 76 references it cites were found in a registry or an index.
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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Flagged first, then by citations
2 of the 76 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 701–800 of 1,008
Polynomial time approximation schemes for geometric k-clustering
Ostrovsky, R., & Rabani, Y. (2002) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2000.892123 · Cited by 46
Efficient nearest-neighbor computation for GPU-based motion planning
Pan, J., Lauterbach, C., & Manocha, D. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5651449 · Cited by 46
Set-based approximate approach for lossless graph summarization
Khan, K. U., Nawaz, W., & Lee, Y.-K. (2015) · Computing · doi:10.1007/s00607-015-0454-9 · Cited by 46
Scalable kNN search on vertically stored time series
Kashyap, S., & Karras, P. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020607 · Cited by 46
Identification with encrypted biometric data
Bringer, J., Chabanne, H., & Kindarji, B. (2010) · Security and Communication Networks · doi:10.1002/sec.206 · Cited by 46
Deep neural architectures for large scale android malware analysis
Nauman, M., Tanveer, T. A., Khan, S. R., & Syed, T. A. (2017) · Cluster Computing · doi:10.1007/s10586-017-0944-y · Cited by 46
Douze, M., Jeǵou, H., & Perronnin, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_48 · Cited by 46
An Efficient Blockchain-Based Privacy-Preserving Collaborative Filtering Architecture
Casino, F., & Patsakis, C. (2019) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2944279 · Cited by 48
Zhang, W., Wei, H., Sisman, B., Dong, X. L., Faloutsos, C., & Page, D. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371813 · Cited by 46
A framework for accurate, efficient private record linkage
Durham, E. (2012) · Cited by 46
New trends in high-D vector similarity search
Echihabi, K., Zoumpatianos, K., & Palpanas, T. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476311.3476407 · Cited by 46
Linear-size approximate voronoi diagrams
Arya, S., & Malamatos, T. (2002) · doi:10.5555/545381.545400 · Cited by 46
SHAPE REPRESENTATIONS AND ALGORITHMS FOR 3D MODEL RETRIEVAL
Kazhdan, M. M. (2004) · Cited by 46
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification
Mu, Y., Hua, G., Fan, W., & 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.2014.130 · Cited by 45
Data-Dependent Hashing Based on p-Stable Distribution
Bai, X. P., Yang, H., Zhou, J. W., Ren, P., & Cheng, J. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2352458 · Cited by 45
Sparsity lower bounds for dimensionality reducing maps
Nelson, J., & NguyÅn, H. L. (2013) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2488608.2488622 · Cited by 45
Scalable Algorithms for Data and Network Analysis
Teng, S. (2016) · now publishers, Inc. eBooks · doi:10.1561/9781680831313 · Cited by 45
A proposed conceptual framework for a representational approach to information retrieval
Lin, J. (2021) · ACM SIGIR Forum · doi:10.1145/3527546.3527552 · Cited by 57
Esser, A., & Bellini, E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97121-2_5 · Cited by 45
Similarity Search in the Blink of an Eye with Compressed Indices
Aguerrebere, C., Bhati, I., Hildebrand, M., Tepper, M., & Willke, T. L. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611479.3611537 · Cited by 44
SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search
Zuo, C., Qiao, M., Zhou, W., Li, F., & Deng, D. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3639324 · Cited by 44
Faster Sieving for Shortest Lattice Vectors Using Spherical Locality-Sensitive Hashing
Laarhoven, T., & de Weger, B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22174-8_6 · Cited by 46
The Johnson-Lindenstrauss Transform: An Empirical Study
Venkatasubramanian, S., & Wang, Q. (2011) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611972917.16 · Cited by 44
Density-preserving projections for large-scale local anomaly detection
de Vries, T., Chawla, S., & Houle, M. E. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0430-4 · Cited by 44
A Segmentation and Graph-Based Video Sequence Matching Method for Video Copy Detection
Liu, H., Lu, H., & Xue, X. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.92 · Cited by 44
Jiang, Y., Wang, J., & Chang, S. (2011) · International Conference on Multimedia Retrieval · doi:10.1145/1991996.1992012 · Cited by 44
A text retrieval approach to content-based audio retrieval
Riley, M., Heinen, E., & Ghosh, J. (2008) · Cited by 44
Leveraging set relations in exact set similarity join
Wang, X., Qin, L., Lin, X., Zhang, Y., & Chang, L. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3099622.3099624 · Cited by 44
MINING PATENTS USING MOLECULAR SIMILARITY SEARCH
RHODES, J., Boyer, S., Kreulen, J. T., Chen, Y., & Ordóñez, P. (2006) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812772435_0029 · Cited by 44
Tracking Web Spam with Hidden Style Similarity
Urvoy, T., Lavergne, T., & Filoche, P. (2006) · Adversarial Information Retrieval on the Web · Cited by 44
Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and Trends
Buchta, R., Gkoktsis, G., Heine, F., & Kleiner, C. (2024) · Digital Threats Research and Practice · doi:10.1145/3696014 · Cited by 51
Optimizing Euclidean Distance Computation
Mussabayev, R. (2024) · Mathematics · doi:10.3390/math12233787 · Cited by 45
Biomechanical simulation and control of hands and tendinous systems
Sachdeva, P., Sueda, S., Bradley, S., Fain, M., & Pai, D. K. (2015) · ACM Transactions on Graphics · doi:10.1145/2766987 · Cited by 43
On the observability of linear systems from random, compressive measurements
Wakin, M. B., Sanandaji, B. M., & Vincent, T. L. (2010) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2010.5718068 · Cited by 43
Scalable techniques for document identifier assignment in inverted indexes
Ding, S., Attenberg, J., & Suel, T. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772723 · Cited by 43
Sha, L., Lucey, P., Yue, Y., Carr, P., Rohlf, C., & Matthews, I. A. (2016) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2856767.2856772 · Cited by 43
Cancelable features using log-Gabor filters for biometric authentication
Kaur, H., & Khanna, P. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3652-3 · Cited by 43
Sha, L., Lucey, P., Yue, Y., Wei, X., Hobbs, J. A., Rohlf, C., & Sridharan, S. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3185596 · Cited by 43
Distributed nearest neighbor classification for large-scale multi-label data on spark
Gonzalez-Lopez, J., Ventura, S., & Cano, A. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.094 · Cited by 43
Sketching and Sublinear Data Structures in Genomics
Marçais, G., Solomon, B., Patro, R., & Kingsford, C. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021156 · Cited by 45
Toussaint, N., Redhead, Y. T., Vidal‐García, M., Vercio, L. L., Liu, W., Fisher, E., Hallgrímsson, B., Tybulewicz, V. L. J., Schnabel, J. A., & Green, J. B. A. (2021) · Development · doi:10.1242/dev.188631 · Cited by 50
On approximating the depth and related problems
Aronov, B. S., & Har-Peled, S. (2005) · doi:10.5555/1070432.1070556 · Cited by 43
Optimal Densification for Fast and Accurate Minwise HashingPreprint
Shrivastava, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.04664 · Cited by 43
L2AP: Fast cosine similarity search with prefix L-2 norm bounds
Anastasiu, D. C., & Karypis, G. (2014) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2014.6816700 · Cited by 42
Efficient logo retrieval through hashing shape context descriptors
Rusiñol, M., & Lladós, J. (2010) · IAPR International Workshop on Document Analysis Systems (DAS) · doi:10.1145/1815330.1815358 · Cited by 42
Roy, M., Bose, S. K., Kar, B., Gopalakrishnan, P. K., & Basu, A. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628810 · Cited by 42
Cost-sensitive learning classification strategy for predicting product failures
Frumosu, F. D., Khan, A. R., Schiøler, H., Külahçı, M., Zaki, M., & Rasmussen, P. W. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113653 · Cited by 42
Discrete-Time Signatures and Randomness in Reservoir Computing
Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J., & Teichmann, J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3076777 · Cited by 42
Almost Optimal Explicit Johnson-Lindenstrauss Families
Kane, D. M., Meka, R., & Nelson, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22935-0_53 · Cited by 42
Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping
Najafi, A., Joudaki, A., & Fatemizadeh, E. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2487981 · Cited by 42
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
Schubert, E., Zimek, A., & Kriegel, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18123-3_2 · Cited by 42
Embedding Tree Metrics into Low-Dimensional Euclidean Spaces
Gupta, A. (2000) · Discrete & Computational Geometry · doi:10.1007/s004540010020 · Cited by 42
Dense Fast Random Projections and Lean Walsh Transforms
Liberty, E., Ailon, N., & Singer, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85363-3_40 · Cited by 42
MACS: music audio characteristic sequence indexing for similarity retrieval
Yang, C. (2002) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/aspaa.2001.969558 · Cited by 42
Buhrman, H., Miltersen, P. B., Radhakrishnan, J., & Venkatesh, S. (2000) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/335305.335357 · Cited by 42
Manifold-Based Signal Recovery and Parameter Estimation from Compressive MeasurementsPreprint
Wakin, M. B. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1002.1247 · Cited by 42
Goyal, N., Lifshits, Y., & Schütze, H. (2008) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1341531.1341538 · Cited by 42
Learning Translation Models from Monolingual Continuous Representations
Zhao, K., Hassan, H., & Auli, M. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1176 · Cited by 41
Online Generation of Locality Sensitive Hash Signatures
Durme, B. V., & Lall, A. (2010) · Cited by 41
Semantic-Aware Blocking for Entity Resolution
Wang, Q., Cui, M., & Liang, H. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2468711 · Cited by 41
Sra, S., & Cherian, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23808-6_21 · Cited by 41
Zhang, H., & Zhang, Q. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098003 · Cited by 41
A hybrid cloud read aligner based on MinHash and kmer voting that preserves privacy
Popic, V., & Batzoglou, S. (2017) · Nature Communications · doi:10.1038/ncomms15311 · Cited by 41
Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status
Hersek, S., Pouyan, M. B., Teague, C. N., Sawka, M. N., Millard-Stafford, M. L., Kogler, G. F., Wolkoff, P., & Inan, O. T. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2743562 · Cited by 41
Computational Bioacoustic Scene Analysis
Stowell, D. (2017) · Springer eBooks · doi:10.1007/978-3-319-63450-0_11 · Cited by 41
FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins
Chatzigeorgakidis, G., Καραγιώργου, Σ., Athanasiou, S., & Skiadopoulos, S. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0115-x · Cited by 41
CellAtlasSearch: a scalable search engine for single cells
Srivastava, D., Iyer, A., Kumar, V., & Sengupta, D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky421 · Cited by 41
Android Malware Detection via Graphlet Sampling
Gao, T., Peng, W., Sisodia, D., Saha, T. K., Li, F., & Hasan, M. A. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2880731 · Cited by 43
Fast Code Clone Detection Based on Weighted Recursive Autoencoders
Zeng, J., Ben, K., Li, X., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2938825 · Cited by 43
Large-scale speaker identification
Schmidt, L., Sharifi, M., & Moreno, I. L. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853878 · Cited by 41
High-dimensional descriptor indexing for large multimedia databases
Junior, E. A. D. V., Cord, M., & Philipp‐Foliguet, S. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458181 · Cited by 41
A unifying framework for ℓ 0-sampling algorithms
Cormode, G., & Firmani, D. (2013) · Distributed and Parallel Databases · doi:10.1007/s10619-013-7131-9 · Cited by 41
Efficient semantic search on DHT overlays
Zhu, Y., & Hu, Y. (2007) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2007.01.005 · Cited by 40
Random Projections for Linear Support Vector Machines
Paul, S., Boutsidis, C., Magdon‐Ismail, M., & Petros Drineas, B. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2641760 · Cited by 40
Zhai, J., Lou, Y., & Gehrke, J. E. (2011) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1989323.1989428 · Cited by 40
Sign Cauchy Projections and Chi-Square Kernel
Li, P., Samorodnitsk, G., & Hopcroft, J. E. (2013) · neural information processing systems · Cited by 40
Suen, C., Huang, S., Eksombatchai, C., Sosič, R., & Leskovec, J. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488496 · Cited by 40
Robust Quad-Based Audio Fingerprinting
Sonnleitner, R., & Widmer, G. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2509248 · Cited by 40
Set similarity search beyond MinHash
Christiani, T., & Pagh, R. (2017) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3055399.3055443 · Cited by 40
Semantic Neighbor Graph Hashing for Multimodal Retrieval
Jin, L., Li, K., Hu, H., Qi, G.-J., & Tang, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2776745 · Cited by 40
Wu, Z., & Yu, J. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700833 · Cited by 40
A review of approaches for topic detection in Twitter
Mottaghinia, Z., Feizi‐Derakhshi, M., Farzinvash, L., & Salehpour, P. (2020) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2020.1785019 · Cited by 41
Hua, Y., Shi, Y., Cui, X., & Li, X. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10255-x · Cited by 41
Sequence graph transform (SGT): a feature embedding function for sequence data mining
Ranjan, C., Ebrahimi, S., & Paynabar, K. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00813-0 · Cited by 40
Fast Near Neighbor Search in High-Dimensional Binary Data
Shrivastava, A., & Li, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33460-3_36 · Cited by 40
Locality sensitive hash functions based on concomitant rank order statistics
Eshghi, K., & Rajaram, S. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401921 · Cited by 40
Dimensionality reduction for long duration and complex spatio-temporal queries
Al‐Naymat, G., Chawla, S., & Gudmundsson, J. (2007) · ACM Symposium on Applied Computing · doi:10.1145/1244002.1244095 · Cited by 40
Low latency photon mapping using block hashing
Vincent, C. H., & McCool, M. D. (2002) · doi:10.5555/569046.569059 · Cited by 40
Nearest neighbour group-based classification
Samsudin, N. A., & Bradley, A. P. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.05.010 · Cited by 39
A fast approach for integrating ORB descriptors in the bag of words model
Grana, C., Borghesani, D., Manfredi, M., & Cucchiara, R. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2008460 · Cited by 39
Zhang, X., Qin, J., Wang, W., Sun, Y., & Lu, J. (2013) · International Conference on Scientific and Statistical Database Management · doi:10.1145/2484838.2484842 · Cited by 39
Set Reconciliation via Counting Bloom Filters
Guo, D., & Li, M. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.215 · Cited by 39
RAPID detection of gene–gene interactions in genome-wide association studies
Brinza, D., Schultz, M., Tesler, G., & Bafna, V. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq529 · Cited by 40
Fast Exact Nearest Patch Matching for Patch-Based Image Editing and Processing
Xiao, C., Liu, M., Yongwei, N., & Dong, Z. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.226 · Cited by 39
Hashing Based Fast Palmprint Identification for Large-Scale Databases
Yue, F., Li, B., Yu, M., & Wang, J. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2253321 · Cited by 39
A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction
Laohakiat, S., Phimoltares, S., & Lursinsap, C. (2016) · Information Sciences · doi:10.1016/j.ins.2016.11.018 · Cited by 39
Hash Bit Selection for Nearest Neighbor Search
Liu, X., He, J., & Chang, S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2695895 · Cited by 39
Crowd-sourced pictures geo-localization method based on street view images and 3D reconstruction
Cheng, L., Yuan, Y., Xia, N., Chen, S., Chen, Y., Yang, K., Ma, L., & Li, M. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.006 · Cited by 39
RUPEE: A fast and accurate purely geometric protein structure search
Ayoub, R., & Lee, Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213712 · Cited by 39
Lattice Sieving via Quantum Random Walks
Chailloux, A., & Loyer, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92068-5_3 · Cited by 39
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review