0 ×

**KNIME Base Nodes** version **4.3.2.v202103021015** by **KNIME AG, Zurich, Switzerland**

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.

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

- Covariance matrix of the input columns
- 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).

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). - Model holding the PCA transformation used by the PCA Apply node to apply the transformation to, e.g. another validation set.

- Normalizer (24 %)
- PCA (19 %)
- Missing Value (7 %)
- Column Filter (4 %) Streamable
~~Excel Reader (XLS)~~(4 %) StreamableDeprecated- Show all 83 recommendations

- PCA Apply (56 %) Streamable
- PCA Inversion (7 %) Streamable
~~Excel Writer (XLS)~~(6 %) Deprecated- Scatter Plot (2 %)
- Column Filter (2 %) Streamable
- Show all 91 recommendations

- 02_NIR_Spectral_Data_Analysis_Clustering_and_Visualization (KNIME Hub)
- 02_NIR_Spectral_Data_Analysis_Clustering_and_Visualization (KNIME Hub)
- 02_Techniques_for_Dimensionality_Reduction (KNIME Hub)
- 03_Dimensionality_Reduction_solution (KNIME Hub)
- 04_Dimensionality_Reduction_solution (KNIME Hub)
- Show all 20 workflows

To use this node in KNIME, install KNIME Base nodes from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter, or chat on Gitter!

Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.