Timeline for concentration of random matrices involving normal random variables
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 26, 2014 at 17:45 | comment | added | mohi | I can now actually do o(nlog^2 n) by truncation I think. I would like o(n) but I would actually be happy with an argument that gives o(nlog n) | |
| Mar 25, 2014 at 8:41 | comment | added | zouzias | I see. Your bound O(n (log n)^3) is tight up to a (logn)^2 factor for general rank-one samples. What are you shooting for? O(n) number of samples? | |
| Mar 24, 2014 at 17:25 | comment | added | mohi | Thanks, I am familiar with all of these references. In some sense all of them require that the matrices are bounded. Hence, one has to apply truncation first. This is where I loose the log factors. | |
| Mar 23, 2014 at 16:32 | history | answered | zouzias | CC BY-SA 3.0 |