Kernel Recursive Least-Squares Tracker for Time-Varying Regression

@article{Vaerenbergh2012KernelRL,
  title={Kernel Recursive Least-Squares Tracker for Time-Varying Regression},
  author={Steven Van Vaerenbergh and Miguel L{\'a}zaro-Gredilla and Ignacio Santamar{\'i}a},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2012},
  volume={23},
  pages={1313-1326}
}
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… CONTINUE READING
Highly Cited
This paper has 151 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 71 extracted citations

152 Citations

02040'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 152 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 26 references

Adaptive Filter Theory

  • S. Haykin
  • 2001
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…