1.0 - 8.00 - Principal Component Analysis (PCA) Functions - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.0
8.00
Release Date
May 2019
Content Type
Programming Reference
Publication ID
B700-4003-098K
Language
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.

Function Description
PCA Composed of PCAMap and PCAReduce. Uses deterministic algorithm to identify principal components of input table in dense format.
PCAScore Takes data in dense format and projects it onto specified subset of principal components identified by PCA.