Start » Filter Reference » Data Classification » Principal Component Analysis » ApplyPCATransform
Expert| Module: | FoundationPro |
|---|
Applies previously obtained Principal Component Analysis (PCA) transformation coefficients to new data.
| Name | Type | Description | |
|---|---|---|---|
 |
inMatrix | Matrix | Input data with variables in columns and examples in rows. |
|
inPCAModel | PCAModel | Previously created PCA model to apply to data provided in inMatrix. |
 |
outTransformedMatrix | Matrix | Transformed inMatrix. |
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 | Malformed inPCAModel - MeanVector and StandardDeviationVector are not row-vectors in ApplyPCATransform. |
| DomainError | Malformed inPCAModel - MeanVector and StandardDeviationVector have to have the same length in ApplyPCATransform. |
| DomainError | PCAModel does not match - inMatrix column count does not match in ApplyPCATransform. |
| DomainError | PCAModel does not match - PCAFeatureVector dimensions does not correspond to inMatrix dimensions in ApplyPCATransform. |
| DomainError | PCAModel does not match - StandardDeviationVector length is different then inMatrix column count in ApplyPCATransform. |
Complexity Level
This filter is available on Expert Complexity Level.