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CreatePCATransform

Expert

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.