Variants of Principal Components Analysis

Principal components analysis (PCA) is probably the most commonly used transform to perform various tasks in many applications. It produces a set of uncorrelated components according to decreasing magnitude of eigenvalues of a second order-statistics covariance matrix. This paper presents four variants of PCA from an algorithmic implementation aspect, SiMultaneous PCA (SMPCA), ProGressive PCA (PGPCA), Successive PCA (SCPCA) and PRioritized PCA (PRPCA). Except the SMPCA which is the commonly used PCA, all the other three are new developments of the PCA, each of which has its own merits and has not been explored in the literature.