User Documentation for Apache MADlib
Dimensionality Reduction

Detailed Description

Methods for reducing the number of variables in a dataset to obtain a set of principle variables.


 Principal Component Analysis
 Produces a model that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components.
 Principal Component Projection
 Projects a higher dimensional data point to a lower dimensional subspace spanned by principal components learned through the PCA training procedure.