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Elimination of Uninformative Variables for Multivariate Calibration
Abstract
A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a criterion based on the b coefficients, are eliminated. The performance of the method is evaluated on simulated data. Practical aspects are discussed on experimentally obtained near-IR data sets. It is concluded that the elimination of uninformative variables can improve predictive ability.
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Li, H., Liang, Y., Xu, Q., & Cao, D. (2009) · Analytica Chimica Acta · doi:10.1016/j.aca.2009.06.046 · Cited by 1,763
Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., & Hamprecht, F. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-213 · Cited by 1,308
Fumagalli, M., Sironi, M., Pozzoli, U., Ferrer-Admettla, A., Pattini, L., & Nielsen, R. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002355 · Cited by 600
Cai, W., Li, Y., & Shao, X. (2008) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2007.10.001 · Cited by 573
Metadata
- Authors
- Centner, V., Massart, D.-L., de Noord, O. E., de Jong, S., Vandeginste, B. M., & Sterna, C.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 1996-11-01
- Volume
- 68
- Issue
- 21
- Pages
- 3851-3858
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2084169316
- Cited by
- 998
- References
- 0
- FWCI
- 10.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Elimination of Uninformative Variables for Multivariate Calibration. https://app.cochat.ai/papers/10.1021/ac960321m
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- Centner, V., Massart, D.-L., de Noord, O. E., de Jong, S., Vandeginste, B. M., & Sterna, C. (1996). Elimination of Uninformative Variables for Multivariate Calibration. Analytical Chemistry. https://doi.org/10.1021/ac960321m
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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.
