Estimation of the Forgetting Factor in Kernel Recursive Least Squares
| Title | Estimation of the Forgetting Factor in Kernel Recursive Least Squares |
| Publication Type | Conference Paper |
| Year of Publication | 2012 |
| Authors | Van Vaerenbergh, S., I. Santamaría, and M. Lázaro-Gredilla |
| Conference Name | 2012 IEEE International Workshop On Machine Learning For Signal Processing (MLSP) |
| Month Published | September |
| Keywords | kernel adaptive filtering |
| Abstract | In a recent work we proposed a kernel recursive least-squares tracker (KRLS-T) algorithm that is capable of tracking in non-stationary environments, thanks to a forgetting mechanism built on a Bayesian framework. In order to guarantee optimal performance its parameters need to be determined, specifically its kernel parameters, regularization and, most importantly in non-stationary environments, its forgetting factor. This is a common difficulty in adaptive filtering techniques and in signal processing algorithms in general. In this paper we demonstrate the equivalence between KRLS-T’s recursive tracking solution and Gaussian process (GP) regression with a specific class of spatio-temporal covariance. This result allows to use standard hyperparameter estimation techniques from the Gaussian process framework to determine the parameters of the KRLS-T algorithm. Most notably, it allows to estimate the optimal forgetting factor in a principled manner. We include results on different benchmark data sets that offer interesting new insights. |
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