Paper “Reducing the Communication and Computational Cost of Random Fourier Features Kernel LMS in Diffusion Networks” published in ICASSP 2023

In this paper, published with PhD candidate Daniel G. Tiglea, Prof. Luis Azpicueta-Ruiz, and Prof. Magno Silva, we propose an extension of the sampling algorithm presented in “A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks” to the adaptive kernel diffusion networks based on random Fourier features.

The idea is to locally adapt the number of nodes censored according to the estimation error, reducing the computational complexity in nonlinear scenarios.

The PDF file can be obtained in this page.


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