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Principal Component Analysis

Select a function from the list below.

Icon	Name	Description
	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.