Locality-Preserving Partial Least Squares Regression
Keyword(s):
The Core
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AbstractThis chapter proposes another nonlinear PLS method, named as locality-preserving partial least squares (LPPLS), which embeds the nonlinear degenerative and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i , two conditions must satisfy: (1) $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i retain the most information about the local nonlinear structure of their respective data sets. (2) The correlation between $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i is the largest. Finally, a quality-related monitoring strategy is established based on LPPLS.
Keyword(s):
1996 ◽
Vol 26
(4)
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pp. 590-600
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2019 ◽
2010 ◽
Vol 21
(5-6)
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pp. 481-494
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2015 ◽
Vol 135
(2)
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pp. 236-243
2012 ◽
Vol 61
(2)
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pp. 277-290
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2018 ◽
Vol 74
(4)
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pp. I_301-I_306
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