Divide Et Impera: How Disentangling Common And Distinctive Variability In Multiset Data Analysis Can Aid Industrial Process Troubleshooting And Understanding

JOURNAL OF CHEMOMETRICS(2021)

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摘要
The possibility of addressing the problem of process troubleshooting and understanding by modelling common and distinctive sources of variation (factorsorcomponents) underlying two sets of measurements was explored in a real-world industrial case study. The used strategy includes a novel approach to systematically detect the number of common and distinctive components. An extension of this strategy for the analysis of a larger number of data blocks, which allows the comparison of data from multiple processing units, is also discussed.
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关键词
canonical correlation analysis (CCA), common components, distinctive components, permutation testing, singular value decomposition (SVD)
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