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Improved Estimation of High-dimensional Ising Models
[article]
2008
arXiv
pre-print
We consider the problem of jointly estimating the parameters as well as the structure of binary valued Markov Random Fields, in contrast to earlier work that focus on one of the two problems. We formulate the problem as a maximization of ℓ_1-regularized surrogate likelihood that allows us to find a sparse solution. Our optimization technique efficiently incorporates the cutting-plane algorithm in order to obtain a tighter outer bound on the marginal polytope, which results in improvement of
arXiv:0811.1239v1
fatcat:jjmxapv77zcw7dsxhzl6pzmo2i