The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A direct LDA algorithm for high-dimensional data — with application to face recognition
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Citations
Cites
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Cited by
Feature selection using Joint Mutual Information Maximisation
Bennasar, M., Hicks, Y., & Setchi, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.007 · Cited by 659
A review on Gabor wavelets for face recognition
Shen, L., & Bai, L. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0033-y · Cited by 462
A unified framework for subspace face recognition
Wang, X., & Tang, X. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.57 · Cited by 359
Face expression recognition system based on ripplet transform type II and least square SVM
Kar, N. B., Babu, K. S., Sangaiah, A. K., & Bakshi, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5485-0 · Cited by 51
Metadata
- Authors
- Yu, H., & Yang, J.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2001-10
- Volume
- 34
- Issue
- 10
- Pages
- 2067-2070
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W1985809919
- Cited by
- 1,578
- References
- 1
- FWCI
- 31.77
- 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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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: A direct LDA algorithm for high-dimensional data — with application to face recognition. https://app.cochat.ai/papers/10.1016/s0031-3203(00)00162-x
- The paper
- Yu, H., & Yang, J. (2001). A direct LDA algorithm for high-dimensional data — with application to face recognition. Pattern Recognition. https://doi.org/10.1016/s0031-3203(00)00162-x
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.
