Binary Graph-Signal Recovery from Noisy Samples
نویسندگان
چکیده
We study the problem of recovering a smooth graph signal from incomplete noisy measurements, using random sampling to choose from a subset of graph nodes. The signal recovery is formulated as a convex optimization problem. The optimality conditions form a system of linear equations which is solvable via Laplacian solvers. In particular, we use an incomplete Cholesky factorization conjugate gradient (ICCG) method for graph signal recovery. Numerical experiments validate the performance of the recovery method over real-world blog-data of 2004 US election.
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