Timeline for Is there an efficient way to compute the "complete subset regression"?
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
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| Oct 19, 2014 at 1:13 | comment | added | meh | In many of these issues, "preferable = quicker". If time wasn't an issue, one would simply compute CSR and find what worked best (after checking for overfitting etc, etc). | |
| Oct 18, 2014 at 20:56 | comment | added | svangen | @aginensky, CSR is one type of regularized regression, but the question has nothing to do with whether it is preferable to other methods. My question is a pure linear algebra question: can the average of the solutions to a large number of linear equation systems (with lots of structure) be computed efficiently? The kind of answer I was hoping for would probably involve some sort of clever matrix identity. Perhaps the "Background" section was not helpful, I will consider editing the question to not mention the application at all, and phrase it as a completely abstract linear algebra question. | |
| Oct 17, 2014 at 23:28 | comment | added | meh | You will probably get other comments suggesting, Cross Validated or stats.exchange etc. as the proper place to ask- and it is. I personally have never heard of such a thing, nor is it clear to me why that would be preferable to some 'regularization regression". I would definitely look at "The Elements of Statistical Learning" by Hastie et al. They certainly discuss similar issues. | |
| Oct 17, 2014 at 22:27 | history | asked | svangen | CC BY-SA 3.0 |