According to this paper, Canonical Discriminant Analysis (CDA) is basically Principal Component Analysis (PCA) followed by Multiple Discriminant Analysis (MDA). I am assuming that MDA is just Multiclass LDA.
My work uses SciKit-Learn's LDA extensively. According to its description, it is
A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
It applies pretty well to cases with multiple classes; can this implementation be also called as MDA?
So to do CDA in Scikit-Learn, should one just apply PCA followed by LDA?