{"title":"PCA Compute","nodeType":"Manipulator","id":"*KrQKjrbm1MZhJvTT","factoryName":"org.knime.base.node.mine.transformation.pca.compute.PCA2ComputeNodeFactory","factoryId":"org.knime.base.node.mine.transformation.pca.compute.PCA2ComputeNodeFactory","path":["Analytics","Mining","PCA"],"owner":"knime","ownerAccountId":"account:team:b5f0848f-496d-4e6a-b869-fa3532248db7","description":"

This node performs a principal component analysis (PCA) on the given input data. The directions of maximal variance (the principal components) are extracted and can be used in the PCA Apply node to project the input into a space of lower dimension while preserving a maximum of information.

\n ","shortDescription":"Principal component analysis computation","keywords":["principal component analysis"],"deprecated":false,"streamable":false,"tags":["Analytics","Mining","PCA"],"icon":"data:image/png;base64,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","views":[],"inPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Table to transform","description":"Input data for the PCA"}],"outPorts":[{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Covariance matrix","description":"Covariance matrix of the input columns"},{"objectClass":"org.knime.core.node.BufferedDataTable","color":"000000","dataType":"Table","optional":false,"name":"Spectral decomposition","description":"Table containing parameters extracted from the PCA. Each row in the table represents one principal component, whereby the rows are sorted with decreasing eigenvalues, i.e. variance along the corresponding principal axis. The first column in the table contains the component's eigenvalue, a high value indicates a high variance (or in other words, the respective component dominates the orientation of the input data).\n
\nEach subsequent column (labeled with the name of the selected input column) contains a coefficient representing the influence of the respective input dimension to the principal component. The higher the absolute value, the higher the influence of the input dimension on the principal component.\n
\nThe mapping of the input rows to, e.g. the first principal axis, is computed as follows (all done in the PCA Apply node): For each dimension in the original space subtract the dimension's mean value and then multiply the resulting vector with the vector given by this table (the first row in the spectral decomposition table to get the value on the first PC, the second row for the second PC and so on)."},{"objectClass":"org.knime.base.node.mine.transformation.port.TransformationPortObject","color":"1eb9dc","dataType":"Transformation","optional":false,"name":"Transformation model","description":"Model holding the PCA transformation used by the PCA Apply node to apply the transformation to, e.g. another validation set."}],"dynInPorts":[],"dynOutPorts":[],"additionalSiteInformation":{"url":"https://update.knime.com/analytics-platform/5.12","trusted":true,"enabledByDefault":true,"name":"KNIME Analytics Platform 5.12 Update Site","version":"5.12.0"},"dialog":[{"sectionName":"Options","fields":[{"name":"Columns","description":"Select the columns that are included in the analysis of principal components, i.e. the original features.","optional":false},{"name":"Fail if missing values are encountered","description":"If checked, execution fails when the selected columns contain missing values. By default, rows with missing values are ignored and excluded from the computation.","optional":false}]}],"links":[],"kudos":[{"accountId":"account:user:e58d314e-fca1-450b-9392-b835228fc4b4","user":"ibrahimakmal12","date":"2026-01-02T22:02:52+00:00"}],"kudosCount":1,"bundleInformation":{"bundleVersion":"5.12.0.v202606261225","bundleName":"KNIME Base Nodes","bundleSymbolicName":"org.knime.base","bundleVendor":"KNIME AG, Zurich, Switzerland","featureSymbolicName":"org.knime.features.base","featureName":"KNIME Base nodes","featureVersion":"5.12.0.v202606261225","featureVendor":"KNIME AG, Zurich, Switzerland","extensionId":"*bjR3r1yWOznPIEXS"},"frequentSuccessors":[]}