Trend filtering on graphs

We introduce a family of adaptive estimators on graphs, based on penalizing the 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 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. ©2016 Yu-Xiang Wang, James Sharpnack, Alex Smola and Ryan Tibshirani.