You are here: Start » AVL.NET » Principal Component Analysis

Principal Component Analysis

Icon Name Description Modules
ApplyPCATransform Applies previously obtained Principal Component Analysis (PCA) transformation coefficients to new data. FoundationPro
CreatePCATransform Performs the Principal Component Analysis (PCA) on provided data, creates the feature vector and normalization coefficients (mean and standard deviation of variables). FoundationPro
MatrixDeterminant Find the determinant of a square matrix. FoundationPro
MatrixPseudoEigenvectors Find the pseudo-eigenvalues and pseudo-eigenvectors of a symmetrical square matrix. FoundationPro
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. FoundationPro
ReversePCATransform Reverses Principal Component Analysis (PCA) process. Can be used to transform data back to original feature space. FoundationPro