The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A new efficient method for determining the number of components in PARAFAC models
Open accessAbstract
A new diagnostic called the core consistency diagnostic (CORCONDIA) is suggested for determining the proper number of components for multiway models. It applies especially to the parallel factor analysis (PARAFAC) model, but also to other models that can be considered as restricted Tucker3 models. It is based on scrutinizing the ‘appropriateness’ of the structural model based on the data and the estimated parameters of gradually augmented models. A PARAFAC model (employing dimension‐wise combinations of components for all modes) is called appropriate if adding other combinations of the same components does not improve the fit considerably. It is proposed to choose the largest model that is still sufficiently appropriate. Using examples from a range of different types of data, it is shown that the core consistency diagnostic is an effective tool for determining the appropriate number of components in e.g. PARAFAC models. However, it is also shown, using simulated data, that the theoretical understanding of CORCONDIA is not yet complete. Copyright © 2003 John Wiley & Sons, Ltd.
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
Cites
PARAFAC. Tutorial and applications
Bro, R. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(97)00032-4 · Cited by 2,814
Self-citationAndersson, C. A., & Bro, R. (2000) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(00)00071-x · Cited by 1,229
Self-citationGeneralized Inverse of Matrices and its Applications
Mayne, A. J. (1972) · Journal of the Operational Research Society · doi:10.1057/jors.1972.96 · Cited by 1,212
Instrumental methods of chemical analysis
Ewing, G. W. (1954) · Cited by 623
Research methods for multimode data analysis
Law, H. G. (1984) · Medical Entomology and Zoology · Cited by 425
PARAFAC2—Part II. Modeling chromatographic data with retention time shifts
Bro, R., Andersson, C. A., & Kiers, H. A. L. (1999) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199905/08)13:3/4<295::aid-cem547>3.0.co;2-y · Cited by 323
Self-citationFactor analysis of tongue shapes
Harshman, R., Ladefoged, P., & Goldstein, L. (1977) · The Journal of the Acoustical Society of America · doi:10.1121/1.381581 · Cited by 228
A simple comprehensive model for the analysis of covariance structures: Some remarks on applications
McDonald, R. P. (1980) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1980.tb00606.x · Cited by 154
Spectrofluorimetric determination of chlorophylls and pheopigments using parallel factor analysis
Moberg, L., Robertsson, G., & Karlberg, B. (2001) · Talanta · doi:10.1016/s0039-9140(00)00650-0 · Cited by 102
Calibration methods for complex second-order data
Smilde, A. K., Tauler, R., Saurina, J., et al. (1999) · Analytica Chimica Acta · doi:10.1016/s0003-2670(99)00374-8 · Cited by 74
Multi-way modelling of high-dimensionality electroencephalographic data
Estienne, F., Matthijs, N., Massart, D. L., Ricoux, P., & Leibovici, D. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00140-x · Cited by 41
Cited by
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,301
Fluorescence spectroscopy and multi-way techniques. PARAFAC
Murphy, K. R., Stedmon, C. A., Graeber, D., & Bro, R. (2013) · Analytical Methods · doi:10.1039/c3ay41160e · Cited by 2,060
Self-citationUnsupervised Multiway Data Analysis: A Literature Survey
Acar, E., & Yener, B. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.112 · Cited by 434
Large-scale cortical networks for hierarchical prediction and prediction error in the primate brain
Chao, Z. C., Takaura, K., Wang, L., Fujii, N., & Dehaene, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.004 · Cited by 238
Gauvin, L., Panisson, A., & Cattuto, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086028 · Cited by 272
Metadata
- Authors
- Bro, R., & Kiers, H. A. L.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2003-05
- Volume
- 17
- Issue
- 5
- Pages
- 274-286
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W1994219736
- Cited by
- 1,257
- References
- 11
- FWCI
- 10.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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
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.
