Timeline for Diffusion kernel "guide"
Current License: CC BY-SA 3.0
11 events
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| Feb 6, 2012 at 15:46 | comment | added | Xodarap | @Deathbreath: I'm looking to cluster, but it's not from a nearest neighbor graph (as I think you're using the term). See edit 2. | |
| Feb 6, 2012 at 15:45 | history | edited | Xodarap | CC BY-SA 3.0 |
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| Feb 6, 2012 at 13:35 | answer | added | Deathbreath | timeline score: 1 | |
| Feb 5, 2012 at 22:27 | comment | added | Deathbreath | It seems to me that you are looking for the nearest neighbor graph of your data, so you can compute the Laplacian and then cluster your data. Is this correct? Maybe you could elaborate on the relationship between your points in $\mathbb{R}^n$ and your graph. | |
| Feb 5, 2012 at 20:07 | comment | added | Xodarap | @Bill: The diffusion kernel is $e^{tH}$ where H is the Laplacian of the graph. But I'm not really interested in the general problem of solving matrix exponentials, I want a sort of "case study" of when using diffusion kernels is [un]helpful. | |
| Feb 4, 2012 at 14:30 | history | tweeted | twitter.com/#!/StackSciComp/status/165804331940057088 | ||
| Feb 3, 2012 at 20:38 | comment | added | Bill Barth | So, that's a little more, but how does the matrix exponential fit in? I think we need more details, still. | |
| Feb 3, 2012 at 16:46 | comment | added | Xodarap | @BillBarth: I have tried to clarify what I'm doing. | |
| Feb 3, 2012 at 16:45 | history | edited | Xodarap | CC BY-SA 3.0 |
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| Feb 3, 2012 at 16:22 | comment | added | Bill Barth | I think you're going to have to explain a little more of the context around diffusion kernels and the paper you cite in order for the question to be intelligible. If you could summarize a bit more what you're trying to compute, I think the answers you'll get will likely be more helpful. | |
| Feb 3, 2012 at 15:43 | history | asked | Xodarap | CC BY-SA 3.0 |