This node applies a projection to the principal components on the given input data. The data model of the PCA computation node can be applied to arbitrary data to reduce it to a given number of dimensions.
The information preservation rates in the selection of the target dimensions give the expected approximation rates based on the training data fed into the connected PCA Compute node. These rates assume that data fed into the predictor is equally distributed as the data the PCA was computed for initially.
- Type: PCAPCA modelPrincipal Components of training data
- Type: TableTable to transformInput data for the PCA