The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Overview and Recent Advances in Partial Least Squares
Overview
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Citations
Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905 · cited 2×
“It has been shown that SIMPLS is equal to PLS1 but differs from PLS2 when applied to the multidimensional matrix Y [8].”
Method
Cited by
Partial least squares regression and projection on latent structure regression (PLS Regression)
Abdi, H. (2010) · WIREs Computational Statistics · doi:10.1002/wics.51 · Cited by 1,440
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012
Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
Chun, H., & Keleş, S. (2010) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2009.00723.x · Cited by 908
Traditional and recent approaches in background modeling for foreground detection: An overview
Bouwmans, T. (2014) · Computer Science Review · doi:10.1016/j.cosrev.2014.04.001 · Cited by 676
Transductive Multi-View Zero-Shot Learning
Fu, Y., Hospedales, T. M., Xiang, T., & Gong, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408354 · Cited by 514
Metadata
- Authors
- Rosipal, R., & Krämer, N.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 34-51
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2137225583
- Cited by
- 1,091
- References
- 5
- FWCI
- 109.87
- Type
- conference paper
- 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: Overview and Recent Advances in Partial Least Squares. https://app.cochat.ai/papers/10.1007/11752790_2
- The paper
- Rosipal, R., & Krämer, N. (2006). Overview and Recent Advances in Partial Least Squares. Lecture Notes in Computer Science. https://doi.org/10.1007/11752790_2
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.
