Simple component analysis based on RV coefficient

Among linear dimensional reduction techniques. Principal Component
Analysis (PCA) presents many optimal properties. Unfortunately, in many applicative
case PCA doesn't produce full interpretable results. For this reason, several
authors proposed methods able to produce sub optimal components but easier to
interpret like Simple Component Analysis (Rousson and Gasser, (2004)). Following
Rousson and Gasser, in this paper we propose to modify the algorithm used for the
Simple Component Analysis by introducing the RV coefficients (SCA-RV) in order
to improve the interpretation of the results.