PCA Apply

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementPCA Apply

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.


Fail if missing values are encountered
If checked, execution fails, when the selected columns contain missing values. By default, rows containing missing values are ignored and not considered in the computation of the principal components.
Target dimensions
Determine the number of dimensions the input data is projected to. The number of target dimensions can either be selected directly or by specifying the minimal amount of information to be preserved. If selected directly, the number of dimensions must be lower or equal than the number of input columns. If the PCA compute node is connected and executed the possible choices for information preservation correspond to the number of dimensions. Each of the choices for the minimum fraction of information to be preserved corresponds to a possible number of dimensions to reduce to.
Replace original data columns
If checked, the columns containing the input data are removed in the output table.

Input Ports

Principal Components of training data
Input data for the PCA

Output Ports

Data projected to its principal components


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