Interface | Description |
---|---|
PCA |
Principal Component Analysis (PCA) is a mathematical procedure that uses an
orthogonal transformation to convert a set of observations of possibly
correlated variables into a set of values of uncorrelated variables called
principal components.
|
Class | Description |
---|---|
PCAbyEigen |
This class performs Principal Component Analysis (PCA) on a data matrix,
using eigen decomposition on the correlation or covariance matrix.
|
PCAbySVD |
This class performs Principal Component Analysis (PCA) on a data matrix,
using the preferred Singular Value Decomposition (SVD) method.
|
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