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|Abstract:||We introduce a new learning algorithm, named smooth-projected neighborhood pursuit, for estimating high dimensional undirected graphs. In particularly, we focus on the nonparanormal graphical model and provide theoretical guarantees for graph estimation consistency. In addition to new computational and theoretical analysis, we also provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Numerical results on both synthetic and real datasets are provided to support our theory.|
|Citation:||Zhao, Tuo, Kathryn Roeder, and Han Liu. "Smooth-projected neighborhood pursuit for high-dimensional nonparanormal graph estimation." In Advances in Neural Information Processing Systems, pp. 162-170. 2012.|
|Pages:||162 - 170|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Advances in Neural Information Processing Systems|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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