Principal component analysis
	Suppose each data point is N-dimensional • Same procedure applies: • The eigenvectors of A define a new coordinate system – eigenvector with largest eigenvalue captures the most variation among training vectors x – eigenvector with smallest eigenvalue has least variation • We can compress the data by only using the top few eigenvectors – corresponds to choosing a “linear subspace” » represent points on a line, plane, or “hyper-plane” – these eigenvectors are known as the principal components