The DOI matches its Crossref record.
Principal Component Analysis with External Information on both Subjects and Variables
Abstract
A method for structural analysis of multivariate data is proposed that combines features of regression analysis and principal component analysis. In this method, the original data are first decomposed into several components according to external information. The components are then subjected to principal component analysis to explore structures within the components. It is shown that this requires the generalized singular value decomposition of a matrix with certain metric matrices. The numerical method based on the QR decomposition is described, which simplifies the computation considerably. The proposed method includes a number of interesting special cases, whose relations to existing methods are discussed. Examples are given to demonstrate practical uses of the method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Matching species traits to environmental variables: a new three-table ordination method
Dolédec, S., Chessel, D., ter Braak, C. J. F., & Champely, S. (1996) · Environmental and Ecological Statistics · doi:10.1007/bf02427859 · Cited by 731
(2004) · Springer Texts in Statistics · doi:10.1007/b98963 · Cited by 517
Ranger, M., Chau, C. M. Y., Garg, A., Woodward, T. S., Beg, M. F., Bjornson, B., Poskitt, K., Fitzpatrick, K., Synnes, A. R., Miller, S. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076702 · Cited by 288
Preference mapping in practice
Greenhoff, K., & MacFie, H. J. H. (1994) · Measurement of Food Preferences · doi:10.1007/978-1-4615-2171-6_6 · Cited by 265
Goghari, V. M., Sanford, N., Spilka, M. J., & Woodward, T. S. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx004 · Cited by 38
Metadata
- Authors
- Takane, Y., & Shibayama, T.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1991-03
- Volume
- 56
- Issue
- 1
- Pages
- 97-120
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W2154388264
- Cited by
- 186
- References
- 0
- FWCI
- 4.01
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Principal Component Analysis with External Information on both Subjects and Variables. https://app.cochat.ai/papers/10.1007/bf02294589
- The paper
- Takane, Y., & Shibayama, T. (1991). Principal Component Analysis with External Information on both Subjects and Variables. Psychometrika. https://doi.org/10.1007/bf02294589
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.
