The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Centering and scaling in component analysis
Abstract
In this paper the purpose and use of centering and scaling are discussed in depth. The main focus is on two‐way bilinear data analysis, but the results can easily be generalized to multiway data analysis. In fact, one of the scopes of this paper is to show that if two‐way centering and scaling are understood, then multiway centering and scaling is quite straightforward. In the literature it is often stated that preprocessing of multiway arrays is difficult, but here it is shown that most of the difficulties do not pertain to three‐ and higher‐way modeling in particular. It is shown that centering is most conveniently seen as a projection step, where the data are projected onto certain well‐defined spaces within a given mode. This view of centering helps to explain why, for example, centering data with missing elements is likely to be suboptimal if there are many missing elements. Building a model for data consists of two parts: postulating a structural model and using a method to estimate the parameters. Centering has to do with the first part: when centering, a model including offsets is postulated. Scaling has to do with the second part: when scaling, another way of fitting the model is employed. It is shown that centering is simply a convenient technique to estimate model parameters for models with certain offsets, but this does not work for all types of offsets. It is also shown that scaling is a way to fit models with a weighted least squares loss function and that sometimes this change in objective function cannot be performed by a simple scaling step. Further practical aspects of and alternatives to centering and scaling are discussed, and examples are used throughout to show that the conclusions in the paper are not only of theoretical interest but can have an impact on practical data analysis. Copyright © 2003 John Wiley & Sons, Ltd.
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Cites
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
Paatero, P., & Tapper, U. (1994) · Environmetrics · doi:10.1002/env.3170050203 · Cited by 6,023
Statistical Analysis With Missing Data
Ghosh, S. (1988) · Technometrics · doi:10.1080/00401706.1988.10488446 · Cited by 4,576
The theory and practice of econometrics
(1986) · Journal of Macroeconomics · doi:10.1016/0164-0704(86)90027-3 · Cited by 4,342
Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra
Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989) · Applied Spectroscopy · doi:10.1366/0003702894202201 · Cited by 3,867
Matrix Analysis for Statistics.
Kafadar, K., & Schott, J. R. (1997) · Journal of the American Statistical Association · doi:10.2307/2965451 · Cited by 919
Research methods for multimode data analysis
Law, H. G. (1984) · Medical Entomology and Zoology · Cited by 425
A weighted non-negative least squares algorithm for three-way ‘PARAFAC’ factor analysis
Paatero, P. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(97)00031-2 · Cited by 340
Helland, I. (1995) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(95)00031-1 · Cited by 296
Comparing alternative approaches for multivariate statistical analysis of batch process data
Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1999) · Journal of Chemometrics · Cited by 240
Maximum likelihood principal component analysis
Wentzell, P. D., Andrews, D. T., Hamilton, D. C., Faber, K., & Kowalski, B. R. (1997) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199707)11:4<339::aid-cem476>3.0.co;2-l · Cited by 236
The parsimony principle applied to multivariate calibration
Seasholtz, M. B., & Kowalski, B. (1993) · Analytica Chimica Acta · doi:10.1016/0003-2670(93)80430-s · Cited by 233
Least Squares Approximation of Matrices by Additive and Multiplicative Models
Gabriel, K. R. (1978) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1978.tb01663.x · Cited by 224
On the rank deficiency and rank augmentation of the spectral measurement matrix
Amrhein, M., Srinivasan, B., Bonvin, D., & Schumacher, M. M. (1996) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(95)00086-0 · Cited by 205
Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms
Kiers, H. A. L. (1997) · Psychometrika · doi:10.1007/bf02295279 · Cited by 199
Missing values in principal component analysis
Grung, B., & Manne, R. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00031-8 · Cited by 154
Data preprocessing and the extended PARAFAC model
Harshman, R. A. (1984) · Medical Entomology and Zoology · Cited by 148
Using the shifted multiplicative model to search for “separability” in crop cultivar trials
Cornelius, P. L., Seyedsadr, M., & Crossa, J. (1992) · Theoretical and Applied Genetics · doi:10.1007/bf00223996 · Cited by 128
Maximum likelihood fitting using ordinary least squares algorithms
Bro, R., Sidiropoulos, N. D., & Smilde, A. K. (2002) · Journal of Chemometrics · doi:10.1002/cem.734 · Cited by 98
Self-citationAndrews, D. T., & Wentzell, P. D. (1997) · Analytica Chimica Acta · doi:10.1016/s0003-2670(97)00270-5 · Cited by 84
van Eeuwijk, F. A. (2024) · doi:10.18174/210306 · Cited by 26
Cited by
Bro, R., & Smilde, A. K. (2014) · Anal. Methods · doi:10.1039/c3ay41907j · Cited by 3,038
Self-citationvan den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-142 · Cited by 2,450
Self-citationAccounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images
Keenan, M. R., & Kotula, P. G. (2004) · Surface and Interface Analysis · doi:10.1002/sia.1657 · Cited by 304
Garali, I., Adanyeguh, I. M., Ichou, F., Perlbarg, V., Seyer, A., Colsch, B., Moszer, I., Guillemot, V., Durr, A., Mochel, F., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx060 · Cited by 68
Metadata
- Authors
- Bro, R., & Smilde, A. K.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2003-01
- Volume
- 17
- Issue
- 1
- Pages
- 16-33
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W2085946009
- Cited by
- 428
- References
- 21
- FWCI
- 6.73
- Type
- journal article
- Language
- en
- Open access
- closed
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