With ever increasing sample sizes across many fields, this approach is likely to be greatly utilized to gain individual-level eigenvectors avoiding the computational challenges associated with ...

“So my first thought was, no, this can’t be true.” Then he thought about it some more. They’d noticed that hard-to-compute terms called “eigenvectors,” describing, in this case, the ways that ...

Those who use computer programs such as CONCOR or NEGOPY, however, may not be aware of this because the eigenvalues or eigenvectors are hidden more or less deeply within the analytic package.

There are two main techniques to implement PCA. The first technique computes eigenvalues and eigenvectors from a covariance matrix derived from the source data. The second PCA technique sidesteps the ...

Correction: The original version of this article incorrectly stated that eigenvalues are the magnitudes of eigenvectors. In fact, eigenvalues are scalars that are multiplied with eigenvectors.