7.00.02 - Principal Component Analysis (PCA) - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)

Principal component analysis (PCA) is a common unsupervised learning technique that is useful for both exploratory data analysis and dimension reduction. PCA is often used as the core procedure for factor analysis.

The PCA function is composed of two functions, PCA_Map and PCA_Reduce.

If the version of PCA_Reduce is AA 6.21 or later, you can input the PCA output to the function PCAPlot.