Bayesian Extensions of Kernel Least Mean Squares
This work provides incremental improvements for adaptive filtering in signal processing and machine learning.
The paper tackled the problem of extending the kernel least mean squares (KLMS) algorithm by linking it to Bayesian filtering, resulting in extensions that add properties like forgetting and discrete data learning while maintaining computational efficiency.
The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that "kernelizes" the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted as an approximate Bayesian filtering method. This allows us to systematically develop extensions of the KLMS by modifying the underlying state-space and observation models. The resulting extensions introduce many desirable properties such as "forgetting", and the ability to learn from discrete data, while retaining the computational simplicity and time complexity of the original algorithm.