Developer Guide



The metrics are computed given the input data meets the following requirements:
  • At least the largest eigenvalue
    is non-zero. Returns error otherwise.
  • Number of eigenvalues
    must be equal to the number of features provided. Returns error if
    is less than the number of features.
The PCA algorithm receives input argument eigenvalues
. It represents the following quality metrics:
  • Explained variance ratio
  • Noise variance
The library uses the following quality metrics:
Quality Metric
Explained variance
Explained variance ratios
Noise variance
- number of principal components,
- number of features in the data set
Quality metrics for PCA are correctly calculated only if the eigenvalues vector obtained from the PCA algorithm has not been reduced. That is, the
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.

Product and Performance Information


Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804