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truncation degree of decomposed covariance matrix
Relation between singular values of a data matrix and the eigenvalues of its covariance matrixHow is a the covariance matrix of a rotated dataset related its SVD and eigenvectors?how to do SVD using the covariance matrix3-sigma Ellipse, why axis length scales with square root of eigenvalues of covariance-matrixSVD - Decomposed Matrix SizesAsymptotic variance of estimators of regression coefficientsWhat is the inverse of the covariance matrix generated by the exponential covariance function?Computing mean square error for linear transformCovariance Matrix UnderstandingRank of covariance matrix
$begingroup$
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 $textbfd=textbfGm$ when doing least squares inversions.
Does anyone know a good technique I could use to determine the truncation level of a decomposed covariance matrix?
covariance svd
asked Mar 22 at 9:55
s144117s144117
11
$endgroup$
add a comment |
$begingroup$
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 $textbfd=textbfGm$ when doing least squares inversions.
Does anyone know a good technique I could use to determine the truncation level of a decomposed covariance matrix?
covariance svd
asked Mar 22 at 9:55
s144117s144117
11
$endgroup$
add a comment |
$begingroup$
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 $textbfd=textbfGm$ when doing least squares inversions.
Does anyone know a good technique I could use to determine the truncation level of a decomposed covariance matrix?
covariance svd
asked Mar 22 at 9:55
s144117s144117
11
$endgroup$
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 $textbfd=textbfGm$ when doing least squares inversions.
Does anyone know a good technique I could use to determine the truncation level of a decomposed covariance matrix?
covariance svd
covariance svd
asked Mar 22 at 9:55
s144117s144117
11
asked Mar 22 at 9:55
s144117s144117
11
asked Mar 22 at 9:55
s144117s144117
11
asked Mar 22 at 9:55
s144117s144117
11
asked Mar 22 at 9:55
s144117s144117
11
11
add a comment |
add a comment |
1 Answer
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$begingroup$
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 $sum_i=1^k sigma_i / sum_i=1^p, sigma_i ge 0.9$.
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
$endgroup$
add a comment |
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$begingroup$
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 $sum_i=1^k sigma_i / sum_i=1^p, sigma_i ge 0.9$.
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
$endgroup$
add a comment |
$begingroup$
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 $sum_i=1^k sigma_i / sum_i=1^p, sigma_i ge 0.9$.
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
$endgroup$
add a comment |
$begingroup$
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 $sum_i=1^k sigma_i / sum_i=1^p, sigma_i ge 0.9$.
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
$endgroup$
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 $sum_i=1^k sigma_i / sum_i=1^p, sigma_i ge 0.9$.
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
answered Mar 23 at 8:54
V. VancakV. Vancak
11.4k3926
11.4k3926
add a comment |
add a comment |
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