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# PrincipalComponents

 PrincipalComponents[matrix] transforms elements of matrix into unscaled principal components.
• The principal components of matrix are linear transformations of the original columns into uncorrelated columns arranged in order of decreasing variance.
 "Covariance" uses covariance method (default) "Correlation" uses correlation method
• If principal components of scaled columns (standardized principal components) are required, the option Method should be used.
• If matrix consists of exact numbers or symbols, the result is also exact or symbolic, respectively.
Principal components of two datasets:
Principal components of two datasets:
 Out[1]=
 Scope   (3)
Principal components computed with arbitrary-precision numbers:
Principal components of exact numbers:
Principal components computation involving symbolic expressions:
 Options   (1)
Principal components using correlation scaling:
The principal component columns are ordered by decreasing variance:
The mean of each principal component column is zero:
The principal component columns are not correlated:
The setting Method yields the same results as standardizing the input matrix:
For certain symbolic matrices the result may be very large:
Align the principal axis of a two-dimensional shape with the horizontal axis:
New in 8