Indirect Gaussian Graph Learning beyond Gaussianity

This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we utilize an additive over-parametrization with shrinkage to incorporate variable dependencies into t...

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Bibliographic Details
Published inarXiv.org
Main Authors She, Yiyuan, Tang, Shao, Zhang, Qiaoya
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.11.2019
Subjects
Dependence
Divergence
Error analysis
Gaussian distribution
Machine learning
Parameterization
Shrinkage
Statistical analysis
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Summary:This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we utilize an additive over-parametrization with shrinkage to incorporate variable dependencies into the criterion. An iterative Gaussian graph learning algorithm is proposed with ease in implementation. Statistical analysis shows that the estimators achieve satisfactory accuracy with the error measured in terms of a proper Bregman divergence. Real-life examples in different settings are given to demonstrate the efficacy of the proposed methodology.
ISSN:2331-8422