Timeline for t-Stochastic Neighbor Embedding vs Topological Data Analysis
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Apr 7, 2017 at 17:43 | history | edited | Alex R. | CC BY-SA 3.0 |
added 4 characters in body
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| Apr 7, 2017 at 15:38 | answer | added | Gurjeet Singh | timeline score: 10 | |
| Apr 7, 2017 at 1:00 | comment | added | Alex R. | @SteveHuntsman: Thanks! TPE looks interesting in how it better preserves density than tSNE. However, it looks like TPE is O(n^3) whereas the Barnes Hut implementation of tSNE is O(n*log(n)) making it much more attractive. I forget the complexity of TDA but if I recall correctly it's at least n^2. So one of the ulterior motives behind my question is to understand how much is lost in terms of topological structure, because it's way faster to run tSNE. | |
| Apr 6, 2017 at 12:08 | comment | added | Steve Huntsman | A different nonlinear dimensionality reduction called tree preserving embedding (dx.doi.org/10.1073/pnas.1018393108) is essentially (i.e., up to practical details) functorial in the sense of Carlsson and Memoli. I have slides detailing this; email me if you want them. | |
| Apr 5, 2017 at 18:40 | history | asked | Alex R. | CC BY-SA 3.0 |