Generalization of Quantification for PLS Correlation

Title & Authors
Generalization of Quantification for PLS Correlation
Yi, Seong-Keun; Huh, Myung-Hoe;

Abstract
This study proposes a quantification algorithm for a PLS method with several sets of variables. We called the quantification method for PLS with more than 2 sets of data a generalization. The basis of the quantification for PLS method is singular value decomposition. To derive the form of singular value decomposition in the data with more than 2 sets more easily, we used the constraint, $\small{a^ta+b^tb+c^tc=3}$ not $\small{a^ta=1}$, $\small{b^tb=1}$, and $\small{c^tc=1}$, for instance, in the case of 3 data sets. However, to prove that there is no difference, we showed it by the use of 2 data sets case because it is very complicate to prove with 3 data sets. The keys of the study are how to form the singular value decomposition and how to get the coordinates for the plots of variables and observations.
Keywords
Partial Least Squares(PLS);generalization of quantification for PLS correlation;
Language
English
Cited by
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