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An Adaptive Sampling Technique for Graph Diffusion LMS Algorithm

Graph signal processing has attracted attention in the signal processing community, since it is an effective tool to deal with great quantities of interrelated data. Recently, a diffusion algorithm for adaptively learning from streaming graphs signals was proposed. However, it suffers from high computational cost since all nodes in the graph are sampled even in steady state. In this paper, we propose an adaptive sampling method for this solution that allows a reduction in computational cost in steady state, while maintaining convergence rate and presenting a slightly better steady-state performance. We also present an analysis to give insights about proper choices for its adaptation parameters.