GENERALIZED CONSTRAINED PRINCIPAL COMPONENT ANALYSIS WITH EXTERNAL INFORMATION

This paper deals with a non-symmetrical analysis of two multiple data sets to study the structure of dependence among sets of variables that play different roles in the analysis. Moving from a covariance criteria with more sets of criterion variables, authors seek simultaneously different linear combinations of each explanatory variable set in presence of external information about them. This approach can be considered an extension of Generalized Constrained Principal Component Analysis (GCPCA) (Amenta and D’Ambra, 1999) in the presence of external information about single or sets of explanatory variables