Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models

@article{Zhang2018LinearTimeAF,
  title={Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models},
  author={Richard Y. Zhang and Salar Fattahi and Somayeh Sojoudi},
  journal={CoRR},
  year={2018},
  volume={abs/1802.04911}
}
The sparse inverse covariance estimation problem is commonly solved using an l1-regularized Gaussian maximum likelihood estimator known as “graphical lasso”, but its computational cost becomes prohibitive for large data sets. A recent line of results showed–under mild assumptions–that the graphical lasso estimator can be retrieved by soft-thresholding the sample covariance matrix and solving a maximum determinant matrix completion (MDMC) problem. This paper proves an extension of this result… CONTINUE READING
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Sparse inverse covariance estimation for chordal structures

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