eigenvectors analysis

What Does Principal Component Analysis Do? Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Why do we use principal component analysis? PCA is the mother method for