The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Framework for Robust Subspace Learning
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Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Simplified neuron model as a principal component analyzer
Oja, E. (1982) · Journal of Mathematical Biology · doi:10.1007/bf00275687 · Cited by 2,309
Robust regression using iteratively reweighted least-squares.
Holland, P. W., & Welsch, R. E. (1977) · Communications in Statistics - Theory and Methods · doi:10.1080/03610927708827533 · Cited by 2,115
Separating Style and Content with Bilinear Models
Tenenbaum, J. B., & Freeman, W. T. (2000) · Neural Computation · doi:10.1162/089976600300015349 · Cited by 840 · cited 2×
“One example is the work of Tenenbaum and Freeman on factoring style and content (Tenenbaum and Freeman, 2000) using a bilinear model.”
MethodA robust principal component analysis
Ruymgaart, F. H. (1981) · Journal of Multivariate Analysis · doi:10.1016/0047-259x(81)90091-9 · Cited by 48
Cited by
Principal component analysis: A review and recent developments
Jolliffe, I. T., & Cadima, J. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0202 · Cited by 10,117
Weighted Nuclear Norm Minimization with Application to Image Denoising
Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.366 · Cited by 2,316
Neera, J., Chen, X., Aslam, N., Wang, K., & Shu, Z. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3126577 · Cited by 42
Sparsity-Aware Learning and Compressed Sensing: An Overview
Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-396502-8.00023-1 · Cited by 14
Metadata
- Authors
- De la Torre, F., & Black, M. J.
- Venue
- International Journal of Computer Vision
- Published
- 2003-08
- Volume
- 54
- Issue
- 1-3
- Pages
- 117-142
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1513013675
- Cited by
- 604
- References
- 8
- FWCI
- 14.02
- 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: A Framework for Robust Subspace Learning. https://app.cochat.ai/papers/10.1023/a:1023709501986
- The paper
- De la Torre, F., & Black, M. J. (2003). A Framework for Robust Subspace Learning. International Journal of Computer Vision. https://doi.org/10.1023/a%3A1023709501986
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.
