Developer Guide and Reference

  • 2021.1
  • 12/04/2020
  • Public Content
Contents

Correlation and Variance-Covariance Matrices

Variance-covariance and correlation matrices are among the most important quantitative measures of a data set that characterize statistical relationships involving dependence.
Specifically, the covariance measures the extent to which variables “fluctuate together” (that is, co-vary). The correlation is the covariance normalized to be between -1 and +1. A positive correlation indicates the extent to which variables increase or decrease simultaneously. A negative correlation indicates the extent to which one variable increases while the other one decreases. Values close to +1 and -1 indicate a high degree of linear dependence between variables.

Details

Given a set
X
of
n
feature vectors LaTex Math image. of dimension
p
, the problem is to compute the sample means and variance-covariance matrix or correlation matrix:
Statistic
Definition
Means
LaTex Math image. , where LaTex Math image.
Variance-covariance matrix
LaTex Math image. , where LaTex Math image. , LaTex Math image. , LaTex Math image.
Correlation matrix
LaTex Math image. , where LaTex Math image. , LaTex Math image. , LaTex Math image.

Computation

The following computation modes are available:

Examples

C++ (CPU)
Java*
There is no support for Java on GPU.
Batch Processing:
Python* with DPC++ support
Batch Processing:
Online Processing:
Python*
Batch Processing:
Online Processing:
Distributed Processing:

Performance Considerations

To get the best overall performance when computing correlation or variance-covariance matrices:
  • If input data is homogeneous, provide the input data and store results in homogeneous numeric tables of the same type as specified in the algorithmFPType class template parameter.
  • If input data is non-homogeneous, use AOS layout rather than SOA layout.
Optimization Notice
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

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.