SAP HANA Predictive Analysis Library (PAL)

Expected Result
PAL_NEW_SCALING_TBL:
Related Information
Scaling Range [page 479]
3.6.7 Principal Component Analysis (PCA)
Principal component analysis (PCA) aims at reducing the dimensionality of multivariate data while accounting
for as much of the variation in the original data set as possible. This technique is especially useful when the
variables within the data set are highly correlated.
Principal components seek to transform the original variables to a new set of variables that are:
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linear combinations of the variables in the data set;
uncorrelated with each other;
ordered according to the amount of variations of the original variables that they explain.
The signs of the columns of the loadings matrix are arbitrary, and may differ between different
implementations for PCA.
Note that if there exists one variable which has constant value across data items, you cannot scale variables
any more.
Prerequisites
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No missing or null data in the inputs.
The data is numeric, not categorical.
PCA
This is a principal component analysis function.
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 457