Principal Component Analysis (PCA) Way of identifying patterns in data   How input basis vectors are correlated for the given data A transformation from a set of (possibly correlated) axes to another set of uncorrelated axes Orthogonal linear transformation (i.e., rotation) New axes are principal components First principal component produces projections that are best in the squared error sense Optimal least squares solution