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Smooth-projected neighborhood pursuit for high-dimensional nonparanormal graph estimation

Author(s): Zhao, T; Roeder, K; Liu, H

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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.
Publication Date: 2012
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.
ISSN: 1049-5258
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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