# Principle Component Analysis - Reduce dimensionality, capture large variation with fewer features, especially when features are highly correlated. - PCA uses an _orthogonal transformation_ to convert a set of observations of possibly correlated variables into linearly uncorrelated variables. - Individual explained variance and cumulative explained variance. - $O(n^3)$ in terms of dimensionality