Topology Adaptive Graph Estimation in High Dimensions
Johannes Lederer, Christian Müller(Submitted on 27 Oct 2014)
We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compare GTREX with standard methods on a new simulation set-up that is designed to assess accurately the strengths and shortcomings of different methods. These simulations show that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperforms other standard methods over a large spectrum of scenarios. Moreover, we show that GTREX can rival this scheme and, therefore, can provide competitive graph estimation without the need for tuning parameter calibration.
Subjects: Machine Learning (stat.ML); Methodology (stat.ME)Cite as: arXiv:1410.7279 [stat.ML] (or arXiv:1410.7279v1 [stat.ML] for this version)