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Indirect Gaussian Graph Learning Beyond Gaussianity
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DOI：10.1109/TNSE.2019.2893383.png)
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Abstract
En 中文
This paper studies how to capture dependency graph structures from real data that may not be multivariate 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.
Keywords:
Additives
Data models
Optimization
Covariance matrices
Random variables
Iterative algorithms
Statistical analysis
Dependency graph learning
random effects
nonconvex optimization
nonasymptotic statistical analysis
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