A Bayesian Approach To Tracking With Kernel Recursive Least-Squares
| Title | A Bayesian Approach To Tracking With Kernel Recursive Least-Squares |
| Publication Type | Conference Paper |
| Year of Publication | 2011 |
| Authors | Lázaro-Gredilla, M., S. Van Vaerenbergh, and I. Santamaría |
| Conference Name | IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011) |
| Month Published | September |
| Conference Location | Beijing, China |
| Keywords | kernel adaptive filtering, KRLS |
| Abstract | In this paper we introduce a kernel-based recursive least-squares (KRLS) algorithm that is able to track nonlinear, time-varying relationships in data. To this purpose we first derive the standard KRLS equations from a Bayesian perspective (including a principled approach to pruning) and then take advantage of this framework to incorporate forgetting in a consistent way, thus enabling the algorithm to perform tracking in non-stationary scenarios. In addition to this tracking ability, the resulting algorithm has a number of appealing properties: It is online, requires a fixed amount of memory and computation per time step and incorporates regularization in a natural manner. We include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm. |
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