On The Relationship Between Online Gaussian Process Regression And Kernel Least Mean Squares Algorithms
| Title | On The Relationship Between Online Gaussian Process Regression And Kernel Least Mean Squares Algorithms |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Van Vaerenbergh, S., J. Fernández-Bes, and V. Elvira |
| Conference Name | 2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016) |
| Month Published | September |
| Publisher | IEEE |
| Conference Location | Salerno, Italy |
| Keywords | gaussian processes, kernel adaptive filtering, KLMS |
| Abstract | We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. The probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences. |
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