PCA: Dimensional Reduction in Eigen

PCA (Principal Component Analisys) is a classical machine learning method to reduce the dimensionality of a problem. PCA involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. Playing with Eigen library, I started to implement PCA in C++.