PCA compute Spectral Decomposition

Dear community,

I am using the PCA compute node. In the spectral decomposition output the row ids represent the eigenvectors, the first column represent the eigenvalues. So far so good.

However, what are the following columns? (Those are named after the columns I have included in the PCA) Loadings? Error, Variance?

Any help is highly appreciated.


Hi @nba,
These extra columns are explained in the node description, should be to the right of the Analytics Platforms’ window (https://docs.knime.com/2018-12/analytics_platform_workbench_guide/index.html).
You can also that online, e.g. on nodepit:

There it says:

Each 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.
The 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).


Hi Gabriel,

Thanks a lot for.
How could I missed that ? :- )