Timeline for Remove noise from data
Current License: CC BY-SA 3.0
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| Aug 30, 2016 at 21:17 | comment | added | Anton Antonov | @Filipe I did not read carefully your comment -- I missed the "image" part/interpretation. Consider posting a new MSE question. I think some modification of this answer of "Cluster a signal into areas of equal intensity" can be applied. | |
| Aug 30, 2016 at 11:46 | comment | added | Anton Antonov | @Filipe It depends what you want do with your 3D data and how it is structured. If you want to remove 3D outliers please see this MSE answer or this blog post "Finding outliers in 2D and 3D numerical data". If you want to do fitting and your data is over a regular grid, then you can apply QR along each X and Y grid line. | |
| Aug 30, 2016 at 10:58 | comment | added | Filipe | Thanks, the QR worked very fine. How can I apply the same method to an image (2 coordinates dimension + 1 intensity dimension) instead of a line (1 coordinate dimension + 1 dimension intensity) like this example? | |
| Aug 29, 2016 at 9:18 | history | edited | Anton Antonov | CC BY-SA 3.0 |
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| Aug 29, 2016 at 9:13 | comment | added | Anton Antonov | Of course QR is robust -- it is its major feature. The point of my answer is more about the application of Quantile Regression to remove outliers. | |
| Aug 29, 2016 at 8:40 | history | edited | Anton Antonov | CC BY-SA 3.0 |
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| Aug 28, 2016 at 21:37 | history | edited | Anton Antonov | CC BY-SA 3.0 |
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| Aug 28, 2016 at 19:05 | comment | added | user484 | For computing the average of a dataset the $q=0.5$ quantile (i.e. the median) is already resistant to outliers (e.g. the median of $\{1,2,3,4,500\}$ is still $3$). Is this no longer true in the case of computing regression curves? What happens if you only run quantile regression once without removing outliers first? | |
| Aug 28, 2016 at 17:47 | history | edited | Karsten7 | CC BY-SA 3.0 |
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| Aug 28, 2016 at 17:35 | history | answered | Anton Antonov | CC BY-SA 3.0 |