Linear Discriminant Analysis, which parameters can be tunned in cross validation set up? I am implementing Linear Discriminant Analysis in R, which parameters can be tunned in cross validation set up? In regularized mode called penalizedLDA there are parameters which are optimised but I want to know which parameters are turned in case of simple LDA method?
LDA has a closed-form solution and therefore has no hyperparameters. The solution can be obtained using the empirical sample class covariance matrix. Shrinkage is used when there are not enough samples. In that case the empirical covariance matrix is often not a very good estimator.