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• W2963670588 abstract "We introduce a family of adaptive estimators on graphs, based on penalizing the l1 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 l2-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." @default.
• W2963670588 created "2019-07-30" @default.
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• W2963670588 date "2016-01-01" @default.
• W2963670588 modified "2023-09-24" @default.
• W2963670588 title "Trend filtering on graphs" @default.
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• W2963670588 doi "https://doi.org/10.5555/2946645.3007058" @default.
• W2963670588 hasPublicationYear "2016" @default.
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