I understand the approach used for partial least squares for regression (PLS regression) where the PLS components are chosen such that the correlation between the scores of the PLS components of the independent variables and the scores of PLS components of the dependent variables is maximized.
I understand the approach for regression when the dependent variables are continuous. In case the dependent variable is categorical then I learned that the approach is termed partial least squares discriminant analysis (PLS-DA). Is that true?
Is it the same thought process as in PLS regression, except that in PLS-DA the dependent variable would have just two values (for binary classification) and we still go ahead and maximize the covariances across two sets of PLS components?