Timeline for Is there a method that is opposite of dimensionality reduction?
Current License: CC BY-SA 3.0
12 events
| when toggle format | what | by | license | comment | |
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| Dec 8, 2022 at 11:07 | comment | added | Prakhar Sharma | 6 years later. I found this paper useful: nature.com/articles/s42256-021-00322-1 | |
| Jul 13, 2015 at 18:49 | comment | added | PhilMacKay | I have discussed with a statistician friend of mine who suggested using kernel PCA on the derivative of my data, since I'm looking for slopes. Would taking the derivative count as "feature engineering"? | |
| Jul 13, 2015 at 18:47 | history | edited | PhilMacKay | CC BY-SA 3.0 |
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| Jul 13, 2015 at 16:20 | comment | added | AN6U5 | Do you know anything about the nonlinearity of the model? Though it may be too complex to simulate, knowing that it is at most made up of degree 3 polynomials restricts the feature engineering significantly e.g. you could add all 3rd degree polys and then PCA it back down to 3D. | |
| Jul 13, 2015 at 3:02 | answer | added | Apurv | timeline score: -2 | |
| Jun 28, 2015 at 11:20 | comment | added | image_doctor | When you say 2 dimensional data, defined by at least three variables, in what sense do you use the term 'variable'? Would classes be a suitable substitution ? It's worth noting that PCA extracts maximally variant dimensions from data, this is not necessarily the most discriminative transform to apply. Have you looked at clustering ? | |
| Jun 26, 2015 at 13:58 | answer | added | conjectures | timeline score: 8 | |
| Jun 26, 2015 at 9:01 | comment | added | Azrael | I am not sure about this, so I am not posting it as an answer. In a neural network type of model, you can keep the hidden layer dimensionality > input layer dimensionality. Then you can use the hidden layer as input to another network/model. But doing so requires lots of data. | |
| Jun 26, 2015 at 2:52 | history | edited | MrMeritology | CC BY-SA 3.0 |
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| Jun 26, 2015 at 0:22 | comment | added | Emre | If I understand correctly, the concept you are looking for is embedding. Look up kernel methods, and kernel PCA in particular. | |
| Jun 25, 2015 at 21:28 | review | First posts | |||
| Jun 26, 2015 at 7:50 | |||||
| Jun 25, 2015 at 21:24 | history | asked | PhilMacKay | CC BY-SA 3.0 |