TY - GEN
T1 - Variants of principal components analysis
AU - Liu, Wei Min
AU - Chang, Chein I.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/82355172879
UR - https://www.scopus.com/pages/publications/82355172879#tab=citedBy
U2 - 10.1109/IGARSS.2007.4422989
DO - 10.1109/IGARSS.2007.4422989
M3 - Conference contribution
AN - SCOPUS:82355172879
SN - 1424412129
SN - 9781424412129
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1083
EP - 1086
BT - 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
T2 - 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Y2 - 23 June 2007 through 28 June 2007
ER -