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

This node performs a principal component analysis (PCA) on the given data. The input data is projected from its original feature space into a space of (possibly) lower dimension with a minimum of information loss.


Target dimensions
Select 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, number of dimensions must be lower or equal than the number of input columns.
Replace original data columns
If checked, the columns containing the input data are removed in the output table and only the coordinates produces by the projection to the principal components remain.
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.
Select columns that are included in the analysis of principal components, i.e the original features.

Input Ports

Input data for the PCA

Output Ports

Table with input values projected to their principal components


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