The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Learning the parts of objects by non-negative matrix factorization
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Recognition-by-components: A theory of human image understanding.
Biederman, I. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.115 · Cited by 5,621
Maximum Likelihood Reconstruction for Emission Tomography
Shepp, L. A., & Vardi, Y. (1982) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.1982.4307558 · Cited by 4,429
Bayesian-Based Iterative Method of Image Restoration*
Richardson, W. H. (1972) · Journal of the Optical Society of America · doi:10.1364/josa.62.000055 · Cited by 4,160
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866
Cited by
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Inference and analysis of cell-cell communication using CellChat
Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237
Metagenes and molecular pattern discovery using matrix factorization
Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308531101 · Cited by 2,183
Metadata
- Authors
- Lee, D. D., & Seung, H. S.
- Venue
- Nature
- Published
- 1999-10
- Volume
- 401
- Issue
- 6755
- Pages
- 788-791
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W1902027874
- Cited by
- 13,868
- References
- 8
- FWCI
- 47.91
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
Statistics and arithmetic are recomputed, not judged.
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- CoChat. (2026). Citation check: Learning the parts of objects by non-negative matrix factorization. https://app.cochat.ai/papers/10.1038/44565
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- Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature. https://doi.org/10.1038/44565
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
