Graph signal processing provides an innovative framework to process data on graphs. Graph filters and their inverses have been widely used in denoising, smoothing, sampling, interpolating and learning. Implementation of graph filter and its inverse filtering procedure on spatially distributed networks (SDNs) is a remarkable challenge, as each agent on an SDN is equipped with a data processing subsystem with limited capacity and a communication subsystem with confined range due to engineering limitations. In this talk, I will introduce the graph filter and the associated inverse filtering on a spatially distributed network. I will also introduce iterative distributed algorithms which are applicable for the implementation of inverse filtering on SDNs.
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