SVD is the decomposition of a matrix into its components. PCA is the analysis of a set of eigenvectors. Eigenvectors can come from SVD components or a covarian

SVD is the decomposition of a matrix into its components. PCA is the analysis of a set of eigenvectors. Eigenvectors can come from SVD components or a covariance matrix. source: http://www.eggie5.com/107-svd-and-pca http://www.eggie5.com/107-svd-and-pca