7.00.02 - Principal Component Analysis (PCA) - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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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.