Timeline for How can Kernel functions make a Grassmann manifold into an Euclidean vector space?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Mar 11, 2017 at 7:07 | vote | accept | vphenix | ||
| Mar 10, 2017 at 21:06 | answer | added | Martins Bruveris | timeline score: 4 | |
| Mar 9, 2017 at 22:52 | comment | added | Ryan Budney | I think the authors have chosen some sloppy language. Roughly speaking, they're saying that the standard set up for their machine learning problem is a "vector space". They notice that all they really need is a metric space (which they phrase in terms of kernels). Once they set up the appropriate metric on the Grassmannian they think of the Grassmannian as being "as good as a vector space". | |
| Mar 9, 2017 at 22:35 | comment | added | vphenix | I added à link to the question. | |
| Mar 9, 2017 at 22:34 | history | edited | vphenix | CC BY-SA 3.0 |
added 144 characters in body
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| Mar 9, 2017 at 22:18 | comment | added | Ryan Budney | Could you provide, either in your question or via a link, a definition of "Grassmannian kernel"? I've tried Googling the term but only a couple papers come up. | |
| Mar 9, 2017 at 22:11 | history | asked | vphenix | CC BY-SA 3.0 |