Variants of principal components analysis

Wei Min Liu, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Pages1083-1086
Number of pages4
DOIs
Publication statusPublished - 2007
Event2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain
Duration: 2007 Jun 232007 Jun 28

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Country/TerritorySpain
CityBarcelona
Period07-06-2307-06-28

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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