Multivariate Modeling Analysis Based on Partial Least Squares Regression and Principal Component Regression
Authors: 
Yulei Chen, Xinwei Zhang, Qi Zou, Hepeng Wang, Shisheng Huang, Liang LuAuthors Info & Claims
Pages 442 - 446
Abstract
In view of the high dimensionality of data in many fields and the serious multiple correlation between variables, this paper proposes an interpretable partial least square regression (PLSR) modeling method. Compared with principal component regression (PCR), when there are a large number of predictors, both PLSR and PCR model the response variables, and the predictors are highly correlated or even collinear. Both of these methods construct new predictors (called components) as linear combinations of the original predictors, but they construct these components in different ways. We use a series of cross-validation experiments to determine the number of components. This paper explores the effectiveness of the above-mentioned two methods. According to the mean square prediction error curve, when the number of components in PLSR is 3 and PCR is 4, better prediction accuracy is obtained.
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Published In
January 2022
551 pages
ISBN:9781450395755
DOI:10.1145/3523286
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Published: 31 May 2022
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BIC 2022
BIC 2022: 2022 2nd International Conference on Bioinformatics and Intelligent Computing
January 21 - 23, 2022
Harbin, China
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