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In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the useful signal belongs to a subspace of much smaller dimension. This motivates smoothing or rank reduction. We formulate a convex optimization problem, where we incorporate a fidelity constraint that prevents the final smoothed estimate from diverging too far from the observations.doi:10.1109/icassp.2008.4518097 dblp:conf/icassp/BarbarossaBS08 fatcat:ftresgpv45bohjqyrpmdvrx7ge