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standard PLS by using NIPALS algorithm.
Inputs:
x n*m matrix
y n*l matrix
Outputs:
t n*max(m,l) matrix
p m*max(m,l) matrix
u n*max(m,l) matrix
q l*max(m,l) matrix
b max(m,l)*max(m,l) matrix
important properties:
x = t*p';
y = u*q';
ti' * tj = 0;
wi' * wj = 0;
refs:
[1] S. J. Qin, "Statistical Process Monitoring: Basics and Beyond," Journal of Chemometrics, vol. 17, pp. 480-502, 2003.
[2] P. Geladi and B. R. Kowalski, "Partial Least Squares Regression: A Tutorial," Analytica Chimica Acta, vol. 185, pp. 1-17, 1986.
Cite As
Yang Zhang (2024). PLS (https://www.mathworks.com/matlabcentral/fileexchange/16465-pls), MATLAB Central File Exchange. Retrieved .
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Version | Published | Release Notes | |
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1.0.0.0 |