The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Fault detection and identification of nonlinear processes based on kernel PCA
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Citations
Cites
Neural networks for pattern recognition
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Applied Multivariate Statistical Analysis.
Johnson, A. R., & Wichern, D. W. (1988) · Biometrics · doi:10.2307/2531616 · Cited by 11,353
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Schölkopf, B., Smola, A., & Müller, K.-R. (1998) · Neural Computation · doi:10.1162/089976698300017467 · Cited by 8,084
An introduction to kernel-based learning algorithms
Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., & Scholkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,473
A User's Guide to Principal Components
Leigh, S., & Jackson, J. E. (1993) · Technometrics · doi:10.2307/1269292 · Cited by 3,332
Nonlinear principal component analysis using autoassociative neural networks
Kramer, M. A. (1991) · AIChE Journal · doi:10.1002/aic.690370209 · Cited by 3,037
Statistical process control of multivariate processes
MacGregor, J. F., & Kourti, T. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)00014-l · Cited by 1,270
Hastie, T., & Stuetzle, W. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478797 · Cited by 1,249
A User's Guide to Principal Components
Freeman, J. (1992) · Journal of the Operational Research Society · doi:10.1057/jors.1992.90 · Cited by 1,018
Multivariate statistical monitoring of process operating performance
Kresta, J. V., Macgregor, J. F., & Marlin, T. E. (1991) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.5450690105 · Cited by 989
Cited by
Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review
Brunner, V., Siegl, M., Geier, D., & Becker, T. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.722202 · Cited by 100
Metadata
- Authors
- Choi, S. W., Lee, C., Lee, J.-M., Park, J. H., & Lee, I.-B.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2005-01
- Volume
- 75
- Issue
- 1
- Pages
- 55-67
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2068193536
- Cited by
- 454
- References
- 19
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Choi, S. W., Lee, C., Lee, J.-M., Park, J. H., & Lee, I.-B. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2004.05.001
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.
