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SCIENTIA SINICA Informationis, Volume 46, Issue 7: 870-882(2016) https://doi.org/10.1360/N112015-00136

Structure learning in graphical models incorporating the scale-free prior

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  • ReceivedOct 7, 2015
  • AcceptedNov 26, 2015

Abstract

In this paper, we consider the problem of structure learning in graphical models under the prior that the underlying networks are scale free. We propose a novel regularization model, which incorporates the scale-free prior, with a penalty that is a hybrid of the Log-type and $L_q$-type penalty functions. An iterative reweighted $L_1$ algorithm is employed to solve the model. Numerical studies show that our method is both effective and practical and performs well in terms of parameter estimation and model selection.


Funded by

国家自然科学基金(11171272)

国家自然科学基金(11571011)


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