# Partial least square

In the context of confirmatory factor analysis, structural equation modeling, & predictor space dimension reduction.

PLS is a supervised dimension reduction procedure, since it summarize the variation of the predictors while, at the same time, requiring the components to have maximum correlation with the response.

Does this statement is enought to prefer PLS over other procedures such as Diagonally Weighted Least Squares or Maximum likelihood?