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Principal Component Analysis
Select a filter from the list below.
| Icon | Name | Description / Applications | Modules | |
|---|---|---|---|---|
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ApplyPCATransform | 
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Applies previously obtained Principal Component Analysis (PCA) transformation coefficients to new data. |
FoundationPro |
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CreatePCATransform |
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Performs the Principal Component Analysis (PCA) on provided data, creates the feature vector and normalization coefficients (mean and standard deviation of variables). |
FoundationPro |
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MatrixDeterminant |
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Find the determinant of a square matrix. |
FoundationPro |
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MatrixPseudoEigenvectors |
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Find the pseudo-eigenvalues and pseudo-eigenvectors of a symmetrical square matrix. |
FoundationPro |
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NormalizeMatrixData |
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Treats Matrix as a data frame, where examples are in rows while columns represent features, and normalizes the data by subtracting mean from each column and dividing it by its standard deviation. |
FoundationPro |
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ReversePCATransform |
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Reverses Principal Component Analysis (PCA) process. Can be used to transform data back to original feature space. |
FoundationPro |
