21. This 50-dimensional space is flattened into a plane using principal components analysis ( PCA ). 22. The simplest approach, however, is to perform a standard principal component analysis ( PCA ). 23. It is conceptually similar to principal component analysis , but applies to categorical rather than continuous data. 24. The higher dimensionality of the descriptor is reduced to 64 through principal components analysis ( PCA ). 25. The indexes have been created by the Australian Bureau of Statistics, the national principal component analysis . 26. Factor analysis is related to principal component analysis ( PCA ), but the two are not identical. 27. Examples are principal component analysis and discriminant analysis, and the quartimax and oblimax methods in factor analysis. 28. Factor analysis is similar to principal component analysis , in that factor analysis also involves linear combinations of variables. 29. More generally, principal component analysis can be used as a method of factor analysis in structural equation modeling. 30. Such vectors are sometimes called the Principal Component Analysis , the basis vectors are not required to be orthogonal.
