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# 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} }

- Published 2018 in ArXiv

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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