Automated Model Selection in Principal Component Analysis: A New Approach Based on the Cross-Validated Ignorance Score
Author:
Affiliation:
1. Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
2. ETH Zürich, Institute of Environmental Engineering, 8093 Zürich, Switzerland
Funder
Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung
Eidgen?ssische Anstalt f?r Wasserversorgung Abwasserreinigung und Gew?sserschutz
Publisher
American Chemical Society (ACS)
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.9b00642
Reference58 articles.
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2. Joliffe, I. Principal Component Analysis, 2nd ed. Springer: New York, 2002; p 487.
3. The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring
4. Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes
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