Nonlinear Dimensionality Reduction by Locally Linear Embedding
Abstract
Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.
Integrity signals
Registry
Cites
Statistics
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?
The idea that the data may lie on a manifold that is only low-dimensional on a local basis is also behind methods such as locally-linear embedding (LLE).
“The idea that the data may lie on a manifold that is only low-dimensional on a local basis is also behind methods such as locally-linear embedding (LLE) 76”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [76]
Cites
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203 · see citing works
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,898 · see citing works
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740 · see citing works
Self-Organization and Associative Memory
Kohonen, T. (1988) · Springer series in information sciences · doi:10.1007/978-3-662-00784-6 · Cited by 3,825 · see citing works
Nonlinear principal component analysis using autoassociative neural networks
Kramer, M. A. (1991) · AIChE Journal · doi:10.1002/aic.690370209 · Cited by 3,037 · see citing works
Data Structures and Network Algorithms
Tarjan, R. E. (1983) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970265 · Cited by 2,073 · see citing works
GTM: The Generative Topographic Mapping
Bishop, C., Svensén, M., & Williams, C. K. I. (1998) · Neural Computation · doi:10.1162/089976698300017953 · Cited by 1,389 · see citing works
Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide
Demmel, J. W., Dongarra, J. J., Ruhe, A., et al. (1987) · Medical Entomology and Zoology · Cited by 1,106 · see citing works
Takane, Y., Young, F. W., & de Leeuw, J. (1977) · Psychometrika · doi:10.1007/bf02293745 · Cited by 954 · see citing works
Hastie, T., & Stuetzle, W. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478797 · Cited by 940 · see citing works
Topology representing networks
Martinetz, T., & Schulten, K. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90109-0 · Cited by 879 · see citing works
Dimension Reduction by Local Principal Component Analysis
Kambhatla, N., & Leen, T. K. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1493 · Cited by 730 · see citing works
Templates for the Solution of Algebraic Eigenvalue Problems
(2000) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719581 · Cited by 673 · see citing works
Image Representations for Visual Learning
Beymer, D., & Poggio, T. (1996) · Science · doi:10.1126/science.272.5270.1905 · Cited by 272 · see citing works
Data visualization by multidimensional scaling: a deterministic annealing approach
Klock, H., & Buhmann, J. M. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00078-3 · Cited by 57 · see citing works
Analysis of Additive Dependencies and Concurvities Using Smallest Additive Principal Components
Donnell, D. J., Buja, A., & Stuetzle, W. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325746 · Cited by 42 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
Dimensionality Reduction by Learning an Invariant Mapping
Hadsell, R., Chopra, S., & LeCun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.100 · Cited by 5,336 · see citing works
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416 · see citing works
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works
Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355 · see citing works
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100 · see citing works
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845 · see citing works
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654 · see citing works
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666 · see citing works
Carlsson, G. (2009) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-09-01249-x · Cited by 2,302 · see citing works
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034 · see citing works
Tufford (2017) · doi:10.1007/s · Cited by 2,003 · see citing works
Bolhuis, P. G., Chandler, D., Dellago, C., & Geissler, P. L. (2002) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.53.082301.113146 · Cited by 1,972 · see citing works
Graph convolutional networks: a comprehensive review
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888 · see citing works
Graph embedding techniques, applications, and performance: A survey
Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801 · see citing works
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675 · see citing works
Sparse coding of sensory inputs
Olshausen, B. A., & Field, D. J. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.07.007 · Cited by 1,404 · see citing works
Cui, P., Wang, X., Pei, J., & Zhu, W. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849727 · Cited by 1,274 · see citing works
Dimensionality reduction for large-scale neural recordings
Cunningham, J. P., & Yu, B. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3776 · Cited by 1,219 · see citing works
Fully memristive neural networks for pattern classification with unsupervised learning
Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106 · see citing works
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065 · see citing works
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958 · see citing works
Feature dimensionality reduction: a review
Jia, W., Sun, M., Lian, J., & Hou, S. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00637-x · Cited by 797 · see citing works
Collaborative representation for hyperspectral anomaly detection
Li, W., & Du, Q. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2343955 · Cited by 758 · see citing works
Network Representation Learning: A Survey
Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714 · see citing works
Perspectives on system identification
Ljung, L. (2010) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2009.12.001 · Cited by 707 · see citing works
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
A Review of Nonlinear Hyperspectral Unmixing Methods
Heylen, R., Parente, M., & Gader, P. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2320576 · Cited by 591 · see citing works
Finding the homology of submanifolds with high confidence from random samples
Niyogi, P., Smale, S., & Weinberger, S. (2008) · Discrete & Computational Geometry · doi:10.1007/s00454-008-9053-2 · Cited by 523 · see citing works
Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition
Singh, G., Memoli, F., & Carlsson, G. (2007) · The Eurographics Association · doi:10.2312/spbg/spbg07/091-100 · Cited by 514 · see citing works
Model learning for robot control: A survey
Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503 · see citing works
LogBERT: Log Anomaly Detection via BERT
Guo, H., Yuan, S., & Wu, X. (2021) · 2021 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534113 · Cited by 454 · see citing works
Lin, T., & Zha, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70735 · Cited by 446 · see citing works
Methodologies for Cross-Domain Data Fusion: An Overview
Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454 · see citing works
Visualizing high-dimensional data: advances in the past decade
Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2640960 · Cited by 427 · see citing works
Testing the manifold hypothesis
Fefferman, C., Mitter, S., & Narayanan, H. (2016) · Journal of the American Mathematical Society · doi:10.1090/jams/852 · Cited by 430 · see citing works
Predicting alzheimer’s disease progression using multi-modal deep learning approach
Lee, G., Nho, K., Kang, B., Sohn, K.-A., Kim, D., Initiative, F. A. D. N., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37769-z · Cited by 406 · see citing works
Fusing local patterns of Gabor magnitude and phase for face recognition
Xie, S., Shan, S., Chen, X., & Chen, J. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2041397 · Cited by 395 · see citing works
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401 · see citing works
Simplifying the representation of complex free-energy landscapes using sketch-map
Ceriotti, M., Tribello, G. A., & Parrinello, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1108486108 · Cited by 340 · see citing works
Das, P., Moll, M., Stamati, H., Kavraki, L. E., & Clementi, C. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603553103 · Cited by 331 · see citing works
Deep learning from temporal coherence in video
Mobahi, H., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553469 · Cited by 328 · see citing works
Data Fusion and Multicue Data Matching by Diffusion Maps
Lafon, S., Keller, Y., & Coifman, R. R. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.223 · Cited by 312 · see citing works
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310 · see citing works
The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging
Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0120 · Cited by 268 · see citing works
Determination of reaction coordinates via locally scaled diffusion map
Rohrdanz, M. A., Zheng, W., Maggioni, M., & Clementi, C. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3569857 · Cited by 292 · see citing works
Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
Cheng, T., & Wang, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2936609 · Cited by 266 · see citing works
Functional, But Not Anatomical, Separation of “What” and “When” in Prefrontal Cortex
Machens, C. K., Romo, R., & Brody, C. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3276-09.2010 · Cited by 260 · see citing works
Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2044788 · Cited by 260 · see citing works
Finding approximate POMDP solutions through belief compression
Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254 · see citing works
Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P. J., Ma, Q., & Zhang, Y. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103899 · Cited by 238 · see citing works
Zhu, X., Suk, H.-I., Wang, L., Lee, S.-W., & Shen, D. (2017) · Medical Image Analysis · doi:10.1016/j.media.2015.10.008 · Cited by 224 · see citing works
Sun, S., Zhu, J., Ma, Y., & Zhou, X. (2019) · Genome biology · doi:10.1186/s13059-019-1898-6 · Cited by 225 · see citing works
Xu, R., Jiang, N., Lin, C., Mrachacz-Kersting, N., Dremstrup, K., & Farina, D. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2294203 · Cited by 219 · see citing works
Magnetic resonance imaging in Alzheimer’s disease neuroimaging initiative 2
Jack, C. R., Barnes, J., Bernstein, M. A., Borowski, B. J., Brewer, J., Clegg, S., Dale, A. M., Carmichael, O., Ching, C., DeCarli, C., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.05.002 · Cited by 220 · see citing works
Neural tuning and representational geometry
Kriegeskorte, N., & Wei, X.-X. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00502-3 · Cited by 187 · see citing works
Zhu, X., Suk, H.-I., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.078 · Cited by 202 · see citing works
Deep learning for small and big data in psychiatry
Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189 · see citing works
Virtual screening of inorganic materials synthesis parameters with deep learning
Kim, E., Huang, K., Jegelka, S., & Olivetti, E. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0055-6 · Cited by 190 · see citing works
Unsupervised machine learning in atomistic simulations, between predictions and understanding
Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5091842 · Cited by 173 · see citing works
Systematic determination of order parameters for chain dynamics using diffusion maps
Ferguson, A. L., Panagiotopoulos, A. Z., Debenedetti, P. G., & Kevrekidis, I. G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003293107 · Cited by 165 · see citing works
Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156 · see citing works
Li, Z., Yan, X., Tian, Z., Yuan, C., Peng, Z., & Li, L. (2013) · Measurement · doi:10.1016/j.measurement.2012.06.013 · Cited by 154 · see citing works
Demonstrating the Transferability and the Descriptive Power of Sketch-Map
Ceriotti, M., Tribello, G. A., & Parrinello, M. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct3010563 · Cited by 126 · see citing works
Topologically-constrained latent variable models
Urtasun, R., Fleet, D. J., Geiger, A., Popović, J., Darrell, T. J., & Lawrence, N. D. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390292 · Cited by 125 · see citing works
Decoding lifespan changes of the human brain using resting-state functional connectivity MRI
Wang, L., Su, L., Shen, H., & Hu, D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044530 · Cited by 115 · see citing works
Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval
Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2652730 · Cited by 108 · see citing works
Jiang, L., Xuan, J., & Shi, T. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.05.017 · Cited by 103 · see citing works
Spectroscopic needs for imaging dark energy experiments
Newman, J. A., Abate, A., Abdalla, F. B., Allam, S., Allen, S. W., Ansari, R., Bailey, S., Barkhouse, W. A., Beers, T. C., Blanton, M. R., et al. (2015) · Astroparticle Physics · doi:10.1016/j.astropartphys.2014.06.007 · Cited by 98 · see citing works
Learning in High Dimension Always Amounts to Extrapolation
Balestriero, R., Pesenti, J., & LeCun, Y. (2021) · arXiv · doi:10.48550/arxiv.2110.09485 · Cited by 93 · see citing works
Automatic 3D Facial Expression Analysis in Videos
Chang, Y., Vieira, M., Turk, M., & Velho, L. (2005) · Lecture Notes in Computer Science · doi:10.1007/11564386_23 · Cited by 74 · see citing works
Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery
Reutlinger, M., & Schneider, G. (2012) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2011.12.006 · Cited by 72 · see citing works
Tong, Z., Li, W., Zhang, B., & Zhang, M. (2018) · Shock and Vibration · doi:10.1155/2018/6714520 · Cited by 64 · see citing works
On the Intrinsic Dimensionality of Image Representations
Gong, S., Boddeti, V. N., & Jain, A. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00411 · Cited by 59 · see citing works
Rolling element bearing fault diagnosis using improved manifold learning
Yao, B., Zhen, P., Wu, L., & Guan, Y. (2017) · IEEE Access · doi:10.1109/access.2017.2693379 · Cited by 48 · see citing works
Hashemi, H., Eisner, J., Rosset, C., Van Durme, B., & Kedzie, C. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.745 · Cited by 47 · see citing works
Boye, A. T., Kristiansen, U. Q., Billinger, M., Nascimento, O. F. D., & Farina, D. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2008.05.001 · Cited by 45 · see citing works
EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model
Yin, J., Qiao, Z., Han, L., & Zhang, X. (2026) · Computer Methods in Biomechanics and Biomedical Engineering · doi:10.1080/10255842.2025.2477801 · Cited by 41 · see citing works
Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces
Sridhar, A., Doyle, S., & Madabhushi, A. (2015) · Journal of Pathology Informatics · doi:10.4103/2153-3539.159441 · Cited by 34 · see citing works
Fault diagnosis of rolling bearings based on marginal Fisher analysis
Jiang, L., Shi, T., & Xuan, J. (2014) · Journal of Vibration and Control · doi:10.1177/1077546312463747 · Cited by 30 · see citing works
Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R., Mamourian, E., Erus, G., Doshi, J., Singh, A., Yang, Z., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5599 · Cited by 28 · see citing works
Steel Surface Defect Classification Based on Discriminant Manifold Regularized Local Descriptor
Zhao, J., Peng, Y., & Yan, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2881962 · Cited by 26 · see citing works
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders
Miolane, N., & Holmes, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01451 · Cited by 16 · see citing works
Self-supervised Regularization for Text Classification
Zhou, M., Li, Z., & Xie, P. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00389 · Cited by 14 · see citing works
Cui, D., & Xia, K. (2018) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-017-2825-3 · Cited by 8 · see citing works
Kenfack, C. S., Mkankam, F. K., Alory, G., Penhoat, Y. D., Hounkonnou, M. N., Vondou, D. A., & Jr., B. G. N. (2017) · Terrestrial, Atmospheric and Oceanic Sciences · doi:10.3319/tao.2016.08.29.01 · Cited by 5 · see citing works
- Authors
- Roweis, S. T., & Saul, L. K.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- AT&T (United States) (US)
- ORCID
- Saul, Lawrence K.
- Venue
- Science
- Published
- 2000-12-22
- Volume
- 290
- Issue
- 5500
- Pages
- 2323-2326
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2053186076
- Cited by
- 14,965
- References
- 16
- Citations by year
- 2026: 238 · 2025: 430 · 2024: 477 · 2023: 610 · 2022: 641 · 2021: 870 · 2020: 892 · 2019: 1005 · 2018: 922 · 2017: 836 · 2016: 819 · 2015: 845 · 2014: 832 · 2013: 806 · 2012: 747
- FWCI
- 81.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- locally linear embedding, nonlinear dimensionality reduction, neighborhood preserving embedding, manifold learning, unsupervised learning, high-dimensional data visualization, face images, text documents
- MeSH terms
- Algorithms, Artificial Intelligence, Face, Humans, Mathematics, Pattern Recognition, Visual
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