Principal Component Analysis

Principal Component Analysis, aka PCA is an unsupervised learning method (sometimes referred to as a dimensionality reduction method) often used to reduce the dimensionality of large datasets. PCA transforms a large number of variables into a reduced number of brand new ones, which are called loading vectors and are fully orthogonal, that is, fully independent from each other.