PCA

Performs the PCA on provided data, create the feature vector and normalization coefficients (mean and standard deviation of variables)

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

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

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

This filter is available on Basic Complexity Level.

This filters is member of PrincipalComponentAnalysis filter group.