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- W2938995741 abstract "In this paper, we study the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of nonconvex regularizers that exhibit superior recovery performance over existing convex ones. We present theoretical results in the form of asymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposed optimization problem and analyze its convergence. Numerical performance of the proposed framework with non-convex regularizers on both synthetic and real-world data are presented for denoising, support recovery, and semi-supervised classification." @default.
- W2938995741 created "2019-04-25" @default.
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- W2938995741 date "2019-05-01" @default.
- W2938995741 modified "2023-10-06" @default.
- W2938995741 title "Improving Graph Trend Filtering with Non-convex Penalties" @default.
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- W2938995741 doi "https://doi.org/10.1109/icassp.2019.8683279" @default.
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