Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 1

Details

The metrics are computed given the input data meets the following requirements:

The PCA algorithm receives input argument eigenvalues . It represents the following quality metrics:

The library uses the following quality metrics:

Quality Metric

Definition

Explained variance



Explained variance ratios



Noise variance



p r - number of principal components, p - number of features in the data set

Note

Quality metrics for PCA are correctly calculated only if the eigenvalues vector obtained from the PCA algorithm has not been reduced. That is, the nComponents parameter of the PCA algorithm must be zero or equal to the number of features. The formulas rely on a full set of the principal components. If the set is reduced, the result is considered incorrect.