Produce a list of summary measures to evaluate the result of the CDPCA
CDpcaSummary(obj)CDpcaSummary returns the following values associated to the loop where the best result was produced:
Number of the loops
Number of iterations
Value of the objective function F
Frobenius norm of the error matrix
Between cluster deviance (percentage)
Explained variance by CDpca components (percentage)
Pseudo Confusion Matrix (if available)
An object of the type produced by CDpca
Eloisa Macedo macedo@ua.pt, Adelaide Freitas adelaide@ua.pt, Maurizio Vichi maurizio.vichi@uniroma1.it
Vichi, M and Saporta, G. (2009). Clustering and disjoint principal component analysis. Computational Statistics and Data Analysis, 53, 3194-3208.
Macedo, E. and Freitas, A. (2015). The alternating least-squares algorithm for CDPCA. Communications in Computer and Information Science (CCIS), Springer Verlag pp. 173-191.