Sparsistency of the Edge Lasso over Graphs


James Sharpnack, Aarti Singh, Alessandro Rinaldo ;
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1028-1036, 2012.


The fused lasso was proposed recently to enable recovery of high-dimensional patterns which are piece-wise constant on a graph, by penalizing the \ell_1-norm of differences of measurements at vertices that share an edge. While there have been some attempts at coming up with efficient algorithms for solving the fused lasso optimization, a theoretical analysis of its performance is mostly lacking except for the simple linear graph topology. In this paper, we investigate \em sparsistency of fused lasso for general graph structures, i.e. its ability to correctly recover the exact support of piece-wise constant graph-structured patterns asymptotically (for large-scale graphs). To emphasize this distinction over previous work, we will refer to it as Edge Lasso. We focus on the (structured) normal means setting, and our results provide necessary and sufficient conditions on the graph properties as well as the signal-to-noise ratio needed to ensure sparsistency. We examplify our results using simple graph-structured patterns, and demonstrate that in some cases fused lasso is sparsistent at very weak signal-to-noise ratios (scaling as \sqrt(\log n)/|A|, where n is the number of vertices in the graph and A is the smallest set of vertices with constant activation). In other cases, it performs no better than thresholding the difference of measurements at vertices which share an edge (which requires signal-to-noise ratio that scales as \sqrt\log n).

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