Principal component analysis

Summary

Principal component analysis replaces correlated variables by new uncorrelated variables, the principal components, ordered by decreasing variance. The components are the coordinates along the eigenvectors of the covariance matrix. Keeping only the first few components gives the best low-dimensional linear summary of the data in the least squares sense. It is the most common method of dimension reduction.