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Highlighting the regularization properties of PLS regression using the conjugate gradient algorithm
Abstract
The link between partial least squares regression and the conjugate gradient algorithm is well known, the goal of this paper is to prove and explore this link to give a simpler proof that PLS regression is a regularization method. Using the conjugate gradient algorithm we will propose a new formula for the PLS filter factors, one that is less sensitive to rounding off error. Finally we will use a simulation study to illustrate the relationship between the amount of regularization and the optimal mean squared error value obtained with PLS regression.
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