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# Principal Component Analysis

Icon | Name | Description |
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ApplyPCATransform | Applies previously obtained Principal Component Analysis (PCA) transformation coefficients to new data. | |

CreatePCATransform | Performs the Principal Component Analysis (PCA) on provided data, creates the feature vector and normalization coefficients (mean and standard deviation of variables). | |

MatrixDeterminant | Find the determinant of a square matrix. | |

MatrixPseudoEigenvectors | Find the pseudo-eigenvalues and pseudo-eigenvectors of a symmetrical square matrix. | |

NormalizeMatrixData | 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. | |

ReversePCATransform | Reverses Principal Component Analysis (PCA) process. Can be used to transform data back to original feature space. |