  For multiple multivariate data sets, we derive conditions under which
Generalized Canonical Correlation Analysis (GCCA) improves classification
performance of the projected datasets, compared to standard Canonical
Correlation Analysis (CCA) using only two data sets. We illustrate our
theoretical results with simulations and a real data experiment.
