Survival kernel with application to kernel adaptive filtering

In this paper, we define a new Mercer kernel, namely survival kernel, which is closely related to our recently proposed survival information potential (SIP). The new kernel function is parameter free, simple in calculation, and strictly positive-definite (SPD) over ℝ^m₊, hence it has potential utility in machine learning especially in online kernel learning. In this work we apply the survival kernel to kernel adaptive filtering, in particular the kernel least mean square (KLMS) algorithm. Simulation results show that KLMS with survival kernel may achieve satisfactory performance with little computational time and without the choice of free parameters.