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. If selected directly, the number of dimensions must be lower than or equal to the number of input columns. If the PCA compute node is connected and executed the possible choices for information preservation are directly mapped to the number of required columns.

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 containing missing values are ignored and not considered during the computation.

Views

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Workflows

• (3D) Fragment library design workflowKNIME Hub
• 01_AnalyticsKNIME Hub
• 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
• 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
• 02_NIR_Spectral_Data_Analysis_Clustering_and_VisualizationKNIME Hub
• Show all 87 workflows

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