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CreatePCATransform


Module: FoundationPro

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

Name Type Range Description
Input value inMatrix Matrix Input data, where variables are in column, and examples are in rows.
Input value inDimensions Integer 1 - How many data dimensions (variables) to be left in transformed data.
Input value inVarianceToLeave Real* 0.0 - 1.0 How many of input data variance should be left in transformed data; overrides inDimensions input.
Output value outPCAModel PCAModel Resulting PCA model.
Output value outTransformedMatrix Matrix Transformed inMatrix with reduced dimensionality.
Diagnostic input diagCovarianceMatrix Matrix Covariance matrix of input data.
Diagnostic input diagNormalizedData Matrix Input data after normalization: scaling and centering.
Diagnostic input diagUsedFeatureIndices IntegerArray Indices of columns in inMatrix, which were used as Principal Components.

Errors

This filter can throw an exception to report error. Read how to deal with errors in Error Handling.

List of possible exceptions:

Error type Description
DomainError Cannot conduct PCA on empty matrix in CreatePCATransform.
DomainError Cannot conduct principal component analysis for 1-row data set in CreatePCATransform.
DomainError Cannot reduce data to less than 1 dimension in CreatePCATransform.
DomainError Could not compute eigenvalues and/or eigenvectors in CreatePCATransform.
DomainError inDimensions has to be lesser then inMatrix column count in PCA filter in CreatePCATransform.
DomainError The process did not converge in CreatePCATransform.
DomainError The provided data did not satisfy the prerequisites in CreatePCATransform.

Complexity Level

This filter is available on Expert Complexity Level.