EM Algorithms for PCA and SPCA

I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. I also introduce a new variant of PC A called sensible principal component analysis (SPCA) which de(cid:173) fines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm.