Multi-set Cannonical Correlation Analysis

This matlab code implements Multi-Set Cannonical Correlation Analysis as explained in the first reference below. It does make a few additions to that simple approach described there. First, it adds regularization by reducing dimensions with PCA in each dataset. This is usefull if the data is noisy or ill-conditioned, i.e. you have lots of dimensions and not as many samples to estimate correlations reliably. Second, it computes the least-squares estimat of the inverse mapping from the CCA subspace back to the original data (called 'forward model' here). These additions are explained in the second reference.

Lucas C. Parra, "Multi-set Canonical Correlation Analysis simply explained", arXiv:1802.03759, Feb 11, 2018..

Lucas C. Parra, Stefan Haufe, Jacek P. Dmochowski, "Correlated Components Analysis --- Extracting Reliable Dimensions in Multivariate Data", Neurons, Behavior, Data Analysis and Theory (NBDT). arXiv:1801.08881. Jan 29, 2019.