Skip to main content

Principal component analysis

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

Perform pca

  • Open a table
  • Run from the top menu: ML | Principal Component Analysis...
  • Select the source table
  • Select feature columns
  • Set the number of principal components
  • Set Center and/or Scale data pre-processing options
  • Run principal component analysis

The result will replace all missing values in all columns and rows.

See also: