Timeline for how to find the singular values of singular value decomposition (SVD) [closed]
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 30 at 13:25 | comment | added | whuber♦ | I still see no question explicitly stated in your comments or your post--only implied questions. You need to be clear about what you wish to ask. | |
| Jun 29 at 1:54 | comment | added | NAFISA | I didnt understand when you said "you have only stated I am confused". My entrie question is not visible? | |
| Jun 29 at 1:53 | comment | added | NAFISA | wait you cant see the whole question? It only displays I am confused? I wrote this "how we find the singular values and therefore condition index number. Some mathematicians say the singular values are the square roots of the eigenvalues of the correlation matrix of the predictors of a model. While others says we use covariance matrix instead. Again some math publications said The singular values are the square roots of the eigenvalues of the square matrix X'X of multiple linear regression model. | |
| Jun 28 at 22:11 | history | closed | whuber♦ | Needs details or clarity | |
| Jun 28 at 22:11 | comment | added | whuber♦ | We have many discussions of this. See stats.stackexchange.com/…. You might find stats.stackexchange.com/questions/259890, stats.stackexchange.com/questions/479485, or even stats.stackexchange.com/questions/154335 to be especially pertinent to your implied question. Indeed, what is your question? You have only stated you are "confused." We can't give an answer to that! | |
| Jun 28 at 19:50 | comment | added | John Madden | What programming environment are you using? It's true that there's a relationship between the singular values of a matrix and that matrix's eigenvalues (if it has any), but numerically it is best to directly compute the singular values rather than going through the eigenvalues as an intermediary. In R, we use "svd" while in python we use "np.linalg.svd" to do this. You apply svd directly to $\mathbf{X}$; no need to form a covariance/gram matrix. | |
| Jun 28 at 19:43 | history | asked | NAFISA | CC BY-SA 4.0 |