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Poster
High-dimensional Nonparanormal Graph Estimation via Smooth-projected Neighborhood Pursuit
Tuo Zhao · Kathryn Roeder · Han Liu

Mon Dec 03 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
in Poster Session and Reception »
We propose a new smooth-projected neighborhood pursuit method for estimating high dimensional undirected graphs. Our method can be viewed as a semiparametric extension of the popular neighborhood pursuit approach proposed by N. Meinshausen and P. B{ü}hlmann 2006 from Gaussian to Gaussian copula models (or the nonparanormal models as proposed by Liu et. al 2009). In terms of methodology and computation, we project a possibly indefinite symmetric matrix into the cone of positive semidefinite matrices. The projection is formulated as a smoothed element-wise $\ell_\infty$-norm minimization problem. We develop an efficient fast proximal gradient algorithm with a provable optimal rate of convergence $\cO(1/\sqrt{\epsilon})$, where $\epsilon$ is the desired accuracy for the objective value. In terms of theory, we provide an alternative view to analyze the trade-off between computational efficiency and statistical error. We give a sufficient condition to secure that the smooth-projected neighborhood pursuit estimator achieves graph estimation consistency. Empirically, we conduct real data experiments on stock and genomic datasets to illustrate the usefulness of the proposed method.
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Author Information

Tuo Zhao (Johns Hopkins University Princeton University)
Kathryn Roeder (Carnegie Mellon University)
Han Liu (Tencent AI Lab)

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