When Is ''Nearest Neighbor'' Meaningful?

Basic local alignment search tool

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669

Color indexing

Swain, M. J., & Ballard, D. H. (1991) · International Journal of Computer Vision · doi:10.1007/bf00130487 · Cited by 5,625

Multivariate Density Estimation

Scott, D. W. (1992) · Wiley series in probability and statistics · doi:10.1002/9780470316849 · Cited by 3,786

Texture features for browsing and retrieval of image data

Manjunath, B. S., & Ma, W. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.531803 · Cited by 3,729

Adaptive Control Processes: A Guided Tour

Bellman, R. E. (1961) · doi:10.1515/9781400874668 · Cited by 2,802

Efficient similarity search in sequence databases

Agrawal, R., Faloutsos, C., & Swami, A. (1993) · Lecture notes in computer science · doi:10.1007/3-540-57301-1_5 · Cited by 1,989

Visual learning and recognition of 3-d objects from appearance

Murase, H., & Nayar, S. K. (1995) · International Journal of Computer Vision · doi:10.1007/bf01421486 · Cited by 1,873

Using discriminant eigenfeatures for image retrieval

Swets, D. L., & Weng, J. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.531802 · Cited by 1,627

Efficient and effective Querying by Image Content

Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petković, D., & Equitz, W. (1994) · Journal of Intelligent Information Systems · doi:10.1007/bf00962238 · Cited by 1,250

V. Adaptive Control Processes

Bellman, R. (1964) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1964.1082221 · Cited by 1,003

VARIQuery: VAE Segment-Based Active Learning for Query Selection in Preference-Based Reinforcement Learning

Marta, D., Holk, S., Pek, C., Tůmová, J., & Leite, I. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341795 · Cited by 14

DDS-NAS: Dynamic data selection within neural architecture search via on-line hard example mining applied to image classification

Poyser, M., & Breckon, T. P. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111948 · Cited by 14

Gerbil - A Novel Software Framework for Visualization and Analysis in the Multispectral Domain

Jordan, J., & Angelopoulou, E. (2010) · doi:10.2312/pe/vmv/vmv10/259-266 · Cited by 13

Reporting Neighbors in High-Dimensional Euclidean Space

Aiger, D., Kaplan, H. Y., & Sharir, M. (2014) · SIAM Journal on Computing · doi:10.1137/12089867x · Cited by 13

Improving vehicle aeroacoustics using machine learning

Kužnar, D., Možina, M., Giordanino, M., & Ivan Bratko, A. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2011.09.023 · Cited by 13

Neuro-logistic Models Based on Evolutionary Generalized Radial Basis Function for the Microarray Gene Expression Classification Problem

Castaño, A., Fernández‐Navarro, F., Hervás‐Martínez, C., & Gutiérrez, P. A. (2011) · Neural Processing Letters · doi:10.1007/s11063-011-9187-8 · Cited by 13

Measuring the principal of defect debt

Akbarinasaji, S., Bener, A., & Erdem, A. (2016) · International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) · doi:10.1145/2896995.2896999 · Cited by 13

Binary Adaptive Embeddings From Order Statistics of Random Projections

Valsesia, D., & Magli, E. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2639036 · Cited by 14

Sentiment analysis of the #MeToo movement using neutrosophy: Application of single-valued neutrosophic sets

Kandasamy, I., Vasantha, W. B., Mathur, N., Bisht, M., & Smarandache, F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819670-0.00006-8 · Cited by 13

A flexible framework for anomaly Detection via dimensionality reduction

Sadr, A. V., Bassett, B. A., & Kunz, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05839-5 · Cited by 13

Structural Health Monitoring and Damage Identification

FUENTES, R., Cross, E. J., Gardner, P. A., Bull, L. A., Rogers, T. J., Barthorpe, R. J., Shi, H., Dervilis, N., Farrar, C. R., & Worden, K. (2020) · Springer eBooks · doi:10.1007/978-1-4939-6503-8_23-1 · Cited by 14

Exploring Multi‐dimensional Data via Subset Embedding

Xie, P., Tao, W., Li, J., Huang, W., & Chen, S. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14290 · Cited by 13

Mining High-Dimensional Data

Wang, W. W., & Yang, J. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_37 · Cited by 13

Zero-Shot SAR Target Recognition Based on Classification Assistance

Wei, Q., Chen, C.-Y., He, M., & He, H. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3258939 · Cited by 13

Global Genotype-Phenotype Correlations in Pseudomonas aeruginosa

Pommerenke, C., Müsken, M., Becker, T., Dötsch, A., Klawonn, F., & Häußler, S. (2010) · PLoS Pathogens · doi:10.1371/journal.ppat.1001074 · Cited by 13

LSH vs Randomized Partition Trees: Which One to Use for Nearest Neighbor Search?

Sinha, K. (2014) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2014.13 · Cited by 13

Metric learning with feature decomposition for image categorization

Wang, M., Liu, B., Tang, J., & Hua, X. (2010) · Neurocomputing · doi:10.1016/j.neucom.2009.08.023 · Cited by 13

Can unbounded distance measures mitigate the curse of dimensionality?

Jayaram, B., & Klawonn, F. (2012) · International Journal of Data Mining Modelling and Management · doi:10.1504/ijdmmm.2012.049883 · Cited by 13

Rank Cover Trees for Nearest Neighbor Search

Houle, M. E., & Nett, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41062-8_3 · Cited by 13

Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass

McRoberts, R. E., Chen, Q., Domke, G. M., Næsset, E., Gobakken, T., Chirici, G., & Mura, M. (2016) · Forestry An International Journal of Forest Research · doi:10.1093/forestry/cpw035 · Cited by 13

Local Partial Least Square classifier in high dimensionality classification

Song, W., Wang, H., Maguire, P. D., & Nibouche, O. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.12.053 · Cited by 13

Discovering visual concept structure with sparse and incomplete tags

Wang, J., Zhu, X., & Gong, S. (2017) · Artificial Intelligence · doi:10.1016/j.artint.2017.05.002 · Cited by 13

Integrating Multidimensional Data for Clustering Analysis With Applications to Cancer Patient Data

Park, S., Xu, H., & Zhao, H. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1730853 · Cited by 13

Fast reciprocal nearest neighbors clustering

López-Sastre, R. J., Oñoro-Rubio, D., Gil-Jiménez, P., & Maldonado-Bascón, S. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.07.023 · Cited by 13

Shared farthest neighbor approach to clustering of high dimensionality, low cardinality data

Rovetta, S., & Masulli, F. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.06.021 · Cited by 13

Comparison study of orthonormal representations of functional data in classification

Meng, Y., Liang, J., & Qian, Y. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.016 · Cited by 13

CoFD: An Algorithm for Non-distance Based Clustering in High Dimensional Spaces*

Shenghuo Zhu, B., Li, T., & Ogihara, M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_6 · Cited by 13

A Low-cost Approach for Effective Shape-based Retrieval and Classification of Medical Images

Felipe, J. C., Olioti, J. B., Traina, A. J. M., Ribeiro, M. X., de Sousa, E. P. M., & Traina, C. (2006) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2005.12 · Cited by 13

Center-Based Indexing in Vector and Metric Spaces

Wojna, A. (2002) · doi:10.5555/964787.964792 · Cited by 13

Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future DirectionsPreprint

Hazard, C. J., Fusting, C. W., Resnick, M., Auerbach, M., Meehan, M., & Korobov, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00246 · Cited by 13

FedID: Enhancing Federated Learning Security Through Dynamic Identification

Huang, S., Li, Y., Chen, C., Gao, Y., & Hu, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3581555 · Cited by 12

Biomonitoring 2.0 Refined: observing local change through metaphylogeography using a community-based eDNA metabarcoding monitoring network

Riley, A. C., Wright, M., Porter, T. M., Maitland, V. C., Baird, D. J., & Hajibabaei, M. (2025) · BMC Biology · doi:10.1186/s12915-025-02284-x · Cited by 12

A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide

Wang, D., Zan, R., Zhu, X., Zhang, Y., Wang, Y., Gu, Y., & Li, Y. (2024) · RSC Advances · doi:10.1039/d4ra04422c · Cited by 12

Measuring the Validity of Clustering Validation Datasets

Jeon, H., Aupetit, M. J. M., Shin, D., Cho, A., Park, S., & Seo, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3548011 · Cited by 12

Efficient Cluster Detection by Ordered Neighborhoods

Aksehirli, E., Goethals, B., & Müller, E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22729-0_2 · Cited by 12

Dimensionality Reduction by Using Sparse Reconstruction Embedding

Huang, S., Cai, C., & Zhang, Y. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15696-0_16 · Cited by 12

The “weight” of models and complexity

Du, J. (2014) · Complexity · doi:10.1002/cplx.21612 · Cited by 12

Amdb: A Design Tool for Access Methods

Kornacker, M., Shah, M. A., & Hellerstein, J. M. (2003) · Cited by 12

An assessment of three variance estimators for the k-nearest neighbour technique

Magnussen, S. (2013) · Silva Fennica · doi:10.14214/sf.925 · Cited by 12

Comparative study on projected clustering methods for hyperspectral imagery classification

Mehta, A., & Dikshit, O. (2015) · Geocarto International · doi:10.1080/10106049.2015.1047416 · Cited by 12

Conditional reliability analysis in high dimensions based on controlled mixture importance sampling and information reuse

Ehre, M., Papaioannou, I., Willcox, K. E., & Straub, D. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113826 · Cited by 12

Lightweight-Yet-Efficient: Revitalizing Ball-Tree for Point-to-Hyperplane Nearest Neighbor Search

Huang, Q., & Tung, A. K. H. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00040 · Cited by 12

Cycle-to-cycle variations in cross-flow turbine performance and flow fields

Snortland, A., Scherl, I., Polagye, B. L., & Williams, O. J. H. (2023) · Experiments in Fluids · doi:10.1007/s00348-023-03725-5 · Cited by 12

A Unified Framework for Linear Dimensionality Reduction in L1

Krahmer, F., & Ward, R. (2015) · Results in Mathematics · doi:10.1007/s00025-015-0475-x · Cited by 12

Easing the Dimensionality Curse by Stretching Metric Spaces

Pola, I. R. V., Traina, A. J. M., & Traina, C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02279-1_30 · Cited by 12

Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data

Aksehirli, E., Goethals, B., Müller, E., & Vreeken, J. (2013) · Proceedings · doi:10.1109/icdm.2013.146 · Cited by 12

Frequent Pattern Mining Algorithms for Data Clustering

Zimek, A., Assent, I., & Vreeken, J. (2014) · Springer eBooks · doi:10.1007/978-3-319-07821-2_16 · Cited by 12

Clustering Large Collection of Biomedical Literature Based on Ontology-Enriched Bipartite Graph Representation and Mutual Refinement Strategy

Yoo, I., & Hu, X. T. (2006) · Lecture notes in computer science · doi:10.1007/11731139_36 · Cited by 12

Convergent Bounds on the Euclidean Distance

Hwang, Y., & Hee-kap Ahn, A. (2011) · Cited by 12

Data preprocessing for distance-based unsupervised Intrusion Detection

Said, D., Stirling, L., Federolf, P., & Barker, K. E. (2011) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2011.5971981 · Cited by 12

A Semantic Triplet Based Story Classifier

Ceran, B., Karad, R., Mandvekar, A., Corman, S., & Davulcu, H. (2012) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2012.97 · Cited by 12

CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

Prasanna, K., Seetha, M., & Kumar, A. (2014) · International Conference on Science Engineering and Management Research · doi:10.1109/icsemr.2014.7043622 · Cited by 12

Machine learning in industrial measurement technology for detection of known and unknown faults of equipment and sensors

Schneider, T., Klein, S., & Schütze, A. (2019) · tm - Technisches Messen · doi:10.1515/teme-2019-0086 · Cited by 12

Anomaly detection method using center offset measurement based on leverage principle

Jia, Q., Chen, C.-X., Gao, X., Li, X., Yan, B., Ai, G., Li, J., & Xu, J. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105191 · Cited by 12

Influence of synchronization within a sensor network on machine learning results

Dorst, T., Robin, Y., Eichstädt, S., Schütze, A., & Schneider, T. (2021) · Journal of sensors and sensor systems · doi:10.5194/jsss-10-233-2021 · Cited by 12

Fuzzy C-Means in High Dimensional Spaces

Winkler, R. E., Klawonn, F., & Kruse, R. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1870-1.ch001 · Cited by 12

SCIS: Combining Instance Selection Methods to Increase Their Effectiveness over a Wide Range of Domains

Caises, Y., González, A., Leyva, E., & Pérez, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04394-9_3 · Cited by 12

On the efficient computation of robust regression estimators

Flores, S. A. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.03.020 · Cited by 12

Dimensionality-reduction frameworks for computationally expensive problems

Tenne, Y., Izui, K., & Nishiwaki, S. (2010) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2010.5586251 · Cited by 12

Reducing Redundancy in Subspace Clustering

Chu, Y.-H., Chen, Y.-J., Yang, D.-N., & Chen⋆, M.-S. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.207 · Cited by 12

Analog Sky Condition Forecasting Based on a k-nn Algorithm

Hall, T. J., Thessin, R. N., Bloy, G. J., & Mutchler, C. N. (2010) · Weather and Forecasting · doi:10.1175/2010waf2222372.1 · Cited by 12

An efficient technique for nearest-neighbor query processing on the spy-tec

Lee, D.-H., & Kim, H.-J. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1245286 · Cited by 12

Testing a multiple machine learning tool (HYDRA) for the bioassessment of fresh waters

Feio, M. J., Viana-Ferreira, C., & Costa, C. M. A. (2014) · Freshwater Science · doi:10.1086/678768 · Cited by 12

Segmentation techniques for the summarization of individual mobility data

Damiani, M. L., & Hachem, F. (2017) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1214 · Cited by 12

Strip Snap Analytics in Cold Rolling Process Using Machine Learning

Chen, Z., Liu, Y., Valera-Medina, A., & Robinson, F. C. J. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8842967 · Cited by 12

Random projection-based auxiliary information can improve tree-based nearest neighbor search

Keivani, O., & Sinha, K. (2020) · Information Sciences · doi:10.1016/j.ins.2020.08.054 · Cited by 12

Multidimensional Descriptor Indexing: Exploring the BitMatrix

Calistru, C., Ribeiro, C., & David, G. (2006) · Lecture notes in computer science · doi:10.1007/11788034_41 · Cited by 12

Fast Search in Large-Scale Image Database Using Vector Quantization

Ye, H., & Xu, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45113-7_47 · Cited by 12

Understanding Multimedia Document Semantics for Cross-Media Retrieval

Wu, F. Q., Yang, Y., Zhuang, Y., & Pan, Y. (2005) · Lecture notes in computer science · doi:10.1007/11581772_87 · Cited by 12

The power-method

Tao, Y., Faloutsos, C., & Dimitris Papadias, C. (2003) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/956863.956881 · Cited by 12

Privacy-Preserving k-NN for Small and Large Data Sets

Amirbekyan, A., & Estivill‐Castro, V. (2007) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2007.67 · Cited by 12

Indexing expensive functions for efficient multi-dimensional similarity search

Chen, H., Liu, J., Furuse, K., Yu, J. X., & Ohbo, N. (2010) · Knowledge and Information Systems · doi:10.1007/s10115-010-0303-2 · Cited by 12

Statistical Association Rules and Relevance Feedback: Powerful Allies to Improve the Retrieval of Medical Images

Ribeiro, M. X., Marques, J., Traina, A. J. M., & Traina, C. (2006) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2006.148 · Cited by 11

BENDLET TRANSFORM BASED OBJECT DETECTION SYSTEM USING PROXIMITY LEARNING APPROACH

Ramalingam, M., & H, N. C. (2022) · International Journal of Advances in Signal and Image Sciences · doi:10.29284/ijasis.8.2.2022.1-8 · Cited by 11

Manifold learning for fMRI time-varying functional connectivity

González-Castillo, J., Fernandez, I. S., Lam, K. C., Handwerker, D. A., Pereira, F., & Bandettini, P. A. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1134012 · Cited by 11

Data Mining Techniques for Outlier Detection

Suri, N., Murty, M. N., & Athithan, G. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2455-9.ch009 · Cited by 11

Machine Learning Methods for Sweet Spot Detection: A Case Study

Hauge, V. L., & Hermansen, G. H. (2017) · Quantitative geology and geostatistics · doi:10.1007/978-3-319-46819-8_38 · Cited by 12

ProSeCo: Visual analysis of class separation measures and dataset characteristics

Bernard, J., Hutter, M., Zeppelzauer, M., Sedlmair, M., & Munzner, T. (2021) · Computers & Graphics · doi:10.1016/j.cag.2021.03.004 · Cited by 11

Performance analysis of ASR system in hybrid DNN-HMM framework using a PWL euclidean activation function

Dutta, A., Gudmalwar, A. P., & Rao, C. V. R. (2021) · Frontiers of Computer Science · doi:10.1007/s11704-020-9419-z · Cited by 11

Performance analysis of dynamic optimization algorithms using relative error distance

van der Stockt, S. A. G., Pamparà, G., Engelbrecht, A. P., & Cleghorn, C. W. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100930 · Cited by 11

The Exploitation of Distance Distributions for Clustering

Thrun, M. C. (2021) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026821500164 · Cited by 11

Prediction of Seismic collapse behavior of deep steel columns using Machine learning

Sediek, O. A., Wu, T., McCormick, J., & El‐Tawil, S. (2022) · Structures · doi:10.1016/j.istruc.2022.04.021 · Cited by 11

Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces

Bernecker, T., Emrich, T., Graf, F., Kriegel, H., Kröger, P., Renz, M., Schubert, E., & Zimek, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13818-8_38 · Cited by 11

Mining Actionable Subspace Clusters in Sequential Data

Sim, K., Poernomo, A. K., & Gopalkrishnan, V. (2010) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972801.39 · Cited by 11

Efficient nearest neighbor query based on extended B+-tree in high-dimensional space

Cui, J., An, Z., Guo, Y. Z., & Zhou, S. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.05.026 · Cited by 11

DEMass: a new density estimator for big data

Ting, K. M., Washio, T., Wells, J. R., Liu, F. T., & Aryal, S. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0612-3 · Cited by 11

High-Dimensional Similarity Searches Using A Metric Pseudo-Grid

Digout, C., & Nascimento, M. A. (2005) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icde.2005.226 · Cited by 11

General Cost Models for Evaluating Dimensionality Reduction in High-Dimensional Spaces

Lian, X., & Chen, L. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.170 · Cited by 11

A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

Jordan, J., Angelopoulou, E., & Maier, A. (2016) · Journal of Electrical and Computer Engineering · doi:10.1155/2016/2635124 · Cited by 11

A highly accurate machine learning approach for developing wireless sensor network middleware

Alshinina, R., & Elleithy, K. M. (2018) · Wireless Telecommunications Symposium · doi:10.1109/wts.2018.8363955 · Cited by 11

Video recommendation using crowdsourced time-sync comments

Ping, Q. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240329 · Cited by 11

Characterizing Strip Snap in Cold Rolling Process Using Advanced Data Analytics

Chen, Z., Liu, Y., Valera-Medina, A., & Robinson, F. C. J. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.078 · Cited by 11

Sublinear Time Nearest Neighbor Search over Generalized Weighted Space

Lei, Y., Huang, Q., Kankanhalli, M., & Tung, A. K. H. (2019) · National University of Singapore · Cited by 11

A Novel Distance Metric Based on Differential Evolution

Ertuğrul, Ö. F. (2019) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-019-04003-5 · Cited by 11

Dimensionality's Blessing: Clustering Images by Underlying Distribution

Lin, W.-Y., Lai, J., Liu, S., & Matsushita, Y. (2018) · 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.2018.00606 · Cited by 11

Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks

Tsiligkaridis, T. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9414153 · Cited by 11

Weighted dimensionality reduction and robust Gaussian mixture model based cancer patient subtyping from gene expression data

Rafique, O., & Mir, A. H. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103620 · Cited by 11

Machine Learning for Supplementing Behavioral Assessment

Bailey, J. D., Baker, J. C., Rzeszutek, M. J., & Lanovaz, M. J. (2021) · Perspectives on Behavior Science · doi:10.1007/s40614-020-00273-9 · Cited by 11

SPARTA: A Laser Probing Approach for Trojan Detection

Stern, A., Mehta, D., Tajik, S., Farahmandi, F., & Tehranipoor, M. M. (2020) · IEEE International Test Conference (ITC) · doi:10.1109/itc44778.2020.9325222 · Cited by 11

Uncertainty-aware automated machine learning toolbox

Dorst, T., Schneider, T., Eichstädt, S., & Schütze, A. (2022) · tm - Technisches Messen · doi:10.1515/teme-2022-0042 · Cited by 11

When Is Nearest Neighbors Indexable?

Shaft, U., & Ramakrishnan, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30570-5_11 · Cited by 22

Theory of nearest neighbors indexability

Shaft, U., & Ramakrishnan, R. (2006) · ACM Transactions on Database Systems · doi:10.1145/1166074.1166077 · Cited by 52

Study on dimensionality curse in the nearest neighbor queries based on statistics

Bo, S., Li, S., & Chong-guang, Z. (2006) · Jisuanji gongcheng

Two algorithms for nearest-neighbor search in high dimensions

Kleinberg, J. M. (1997) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/258533.258653 · Cited by 347

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