Timeline for Is removing duplicate data necessary for Gaussian Process Regression (GPR)?
Current License: CC BY-SA 3.0
9 events
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| May 14, 2020 at 19:58 | comment | added | Tomas | You don't have to add small noise. Look at the trick in Rasmussen & Williams 2006, Gaussian Processes for Machine Learning, (3.26): instead of decomposing K, they use matrix inversion lemma and decompose $W^{1/2} K W^{1/2} + I$ instead. Anyway, thanks for confirmation that removing duplicate data is necessary... | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Mar 31, 2016 at 16:55 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Mar 31, 2016 at 16:04 | vote | accept | Buna | ||
| Mar 31, 2016 at 13:05 | history | edited | Sycorax♦ | CC BY-SA 3.0 |
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| Mar 31, 2016 at 12:02 | comment | added | j__ | Ah sorry I must have read that too quickly - a bad habit that I picked up when I started using the app. In that case I agree completely (up vote) | |
| Mar 31, 2016 at 12:01 | comment | added | Sycorax♦ | The question is about non-noisy observations. | |
| Mar 31, 2016 at 11:48 | comment | added | j__ | I don't think this is correct in all cases. It is very common to assume your observations have gaussian noise for example. Then the noise will be reduced from these combined observations. In this case the kernel matrix will remain full rank also. | |
| Mar 31, 2016 at 2:54 | history | answered | Sycorax♦ | CC BY-SA 3.0 |