Kernel Recursive Least-Squares Tracker for Time-Varying Regression
| Title | Kernel Recursive Least-Squares Tracker for Time-Varying Regression |
| Publication Type | Journal Article |
| Year of Publication | 2012 |
| Authors | Van Vaerenbergh, S., M. Lázaro-Gredilla, and I. Santamaría |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 23 |
| Issue | 8 |
| Pagination | 1313--1326 |
| Month Published | August |
| ISSN | 2162-237X |
| Keywords | kernel adaptive filtering, KRLS |
| Abstract | In this paper, we introduce a kernel 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 sensible 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 nonstationary scenarios. The resulting method is the first kernel adaptive filtering algorithm that includes a forgetting factor in a principled and numerically stable manner. In addition to its tracking ability, it has a number of appealing properties. It is online, requires a fixed amount of memory and computation per time step, incorporates regularization in a natural manner and provides confidence intervals along with each prediction. We include experimental results that support the theory as well as illustrate the efficiency of the proposed algorithm. |
| URL | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6227361 |
| DOI | 10.1109/TNNLS.2012.2200500 |
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