PCA

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.

Options

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.

Columns

Select columns that are included in the analysis of principal components, i.e the original features.

Views

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Workflows

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• Show all 9 workflows

Links

• How to use K-means learner ?by armingrudd on 2019-05-04

  forum.knime.com/p/59956

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