Kernel Recursive Least-Squares Tracker for Time-Varying Regression Articles uri icon


  • Van Vaerenberg, Steven

publication date

  • August 2012

start page

  • 1313

end page

  • 1326


  • 8


  • 23

International Standard Serial Number (ISSN)

  • 2162-237X

Electronic International Standard Serial Number (EISSN)

  • 2162-2388


  • 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.