0
$\begingroup$

I have a covariance matrix of a standardized data set. Doing a singular value decomposition i find near zero singular values and would therefore like to truncate it.

I know of Picard plots which would do the trick. But I have only used it on systems such as $\textbf{d}=\textbf{Gm}$ when doing least squares inversions.

Does anyone know a good technique I could use to determine the truncation level of a decomposed covariance matrix?

$\endgroup$
0
$\begingroup$

Common rule of thumb is truncating the analysis whenever the ratio of the singular values exceeds $0.9$, i.e., stop when you first have $\sum_{i=1}^k \sigma_i / \sum_{i=1}^p, \sigma_i \ge 0.9$.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.