PCA 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.

Options

Target dimensions

Determines 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.

• Dimension(s) to reduce to: Select a fixed number of target dimensions. It must be lower than or equal to the number of dimensions in the transformation model.
• Minimum information fraction: Specify the minimal amount of information to be preserved.

Number of dimensions

The number of dimensions to reduce the data to. Must be lower than or equal to the number of dimensions in the transformation model. If the PCA Compute node is connected and executed, the possible choices for number of dimensions are directly mapped to the percentage of information preserved.

Information preservation (%)

The minimum percentage of information to preserve in the reduced dimensions. If the PCA Compute node is connected and executed, the possible choices for information preservation are directly mapped to the number of output dimensions.

Remove original data columns

If checked, the columns containing the input data are removed.

Fail if missing values are encountered

If checked, execution fails when the selected columns contain missing values. By default, rows with missing values are ignored and excluded from the computation.

Views

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Workflows

• (3D) Fragment library design workflow v1.1KNIME Hub
• 01_AnalyticsKNIME Hub
• 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
• 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
• 02_Techniques_for_Dimensionality_ReductionKNIME Hub
• Show all 150 workflows

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