DocumentCode :
2666666
Title :
Variants of Principal Components Analysis
Author :
Wei-Min Liu ; Chein-I Chang
Author_Institution :
Univ. of Maryland, Baltimore
fYear :
2007
fDate :
23-28 July 2007
Firstpage :
1083
Lastpage :
1086
Abstract :
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.
Keywords :
covariance matrices; geophysical techniques; principal component analysis; PGPCA; PRPCA; SCPCA; SMPCA; covariance matrix; principal components analysis variants; prioritized PCA; progressive PCA; simultaneous PCA; successive PCA; Bridges; Character generation; Covariance matrix; Eigenvalues and eigenfunctions; Equations; Independent component analysis; Performance analysis; Personal communication networks; Polynomials; Principal component analysis; Dimensionality reduction (DR); PRioritized PCA (PRPCA); Principal components analysis (PCA); ProGressive PCA (PGPCA); SiMultaneous PCA (SMPCA); SuCcessive PCA (SCPCA);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
Type :
conf
DOI :
10.1109/IGARSS.2007.4422989
Filename :
4422989
Link To Document :
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