This article presents a from-scratch C# implementation of the first technique: compute eigenvalues and eigenvectors from the covariance matrix. If you're not familiar with PCA, most of the terminology ...

Given a covariance matrix, we consider the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number of nonzero coefficients ...

In particular, they used a "covariance matrix" to flesh out patterns within the data using a statistical technique called principal-component analysis (PCA).

Boaz Nadler, Finite Sample Approximation Results for Principal Component Analysis: A Matrix Perturbation Approach, The Annals of Statistics, Vol. 36, No. 6, High Dimensional Inference and Random ...

The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...

Covariance matrix estimation, crucial for multivariate inference, faces significant challenges when the number of variables rivals or exceeds the sample size.