Original paper
Improving Graph Trend Filtering with Non-convex Penalties
Published: May 1, 2019
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...
Paper Details
Title
Improving Graph Trend Filtering with Non-convex Penalties
Published Date
May 1, 2019
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