Generalization of Quantification for PLS Correlation

Generalization of Quantification for PLS Correlation
  • 이성근
  • 허명회

초록

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, {a^t} a+ {b^t} {b}+{c^t} {c}=3 not a ^{t} a=1, b ^{t} b=1, and c ^{t} c=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.

키워드

Partial Least Squares(PLS)generalization of quantification for PLS correlation.
제목
Generalization of Quantification for PLS Correlation
제목 (타언어)
Generalization of Quantification for PLS Correlation
저자
이성근허명회
발행일
2012
저널명
응용통계연구
25
1
페이지
225 ~ 237