Developer Guide

Contents

Quality Metrics for Principal Components Analysis

Given the results of the PCA algorithm, data set
of eigenvalues in decreasing order, full number of principal components
p
and reduced number of components
p
r
p
the problem is to evaluate the explained variances radio and noise variance.
QualityMetricsId
for the PCA algorithm is
explainedVarianceMetrics
.
For description of the default PCA quality metrics, refer to Details .

Product and Performance Information

1

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