Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani.
Year: 2016, Volume: 17, Issue: 105, Pages: 1−41
We introduce a family of adaptive estimators on graphs, based on penalizing the $\ell_1$ norm of discrete graph differences. This generalizes the idea of trend filtering (Kim et al., 2009; Tibshirani, 2014), used for univariate nonparametric regression, to graphs. Analogous to the univariate case, graph trend filtering exhibits a level of local adaptivity unmatched by the usual $\ell_2$-based graph smoothers. It is also defined by a convex minimization problem that is readily solved (e.g., by fast ADMM or Newton algorithms). We demonstrate the merits of graph trend filtering through both examples and theory.