Back to Adaptive Vision Studio website
You are here: Start » Filter Reference » Principal Component Analysis » ReversePCATransform
ReversePCATransform
Reverses Principal Component Analysis (PCA) process. Can be used to transform data back to original feature space.
| Name | Type | Description | |
|---|---|---|---|
 |
inTransformedMatrix | Matrix | Data that was transformed earlier. |
|
inPCAModel | PCAModel | PCA model used to create inTransformedMatrix. |
 |
outMatrix | Matrix | inTransformedMatrix transformed back to its original feature space. |
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 | Feature vector of inPCAModel and Transformed Data matrices dimensions do not correspond to each other in ReversePCATransform. |
| DomainError | inMeanVector and inStandardDeviationVector have incorrect size for inTransformedMatrix provided in ReversePCATransform. |
| DomainError | Malformed inPCAModel - StandardDeviationVector is not row-vector in ReversePCATransform. |
| DomainError | Malformed inPCAModel - MeanVector is not row-vector in ReversePCATransform. |
| DomainError | Malformed inPCAModel - uneven vector sizes in ReversePCATransform. |
Complexity Level
This filter is available on Expert Complexity Level.
