Abstract
Abstract When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q , of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p . Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined.
Keywords
Principal component analysisNormalization (sociology)Covariance matrixCovarianceCurse of dimensionalityMultivariate statisticsMathematicsDimensionality reductionConfusionSimplicityStatisticsComputer scienceArtificial intelligence
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- 2005
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- other
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Cite This
Ian T. Jolliffe
(2005).
Principal Component Analysis.
Encyclopedia of Statistics in Behavioral Science
.
https://doi.org/10.1002/0470013192.bsa501
Identifiers
- DOI
- 10.1002/0470013192.bsa501