We consider the problem of high-dimensional Ising model selection using neighborhood-based least absolute shrinkage and selection operator (Lasso). It is rigorously proved that under some mild coherence conditions on the population covariance matrix of the Ising model, consistent model selection can be achieved with sample sizes $n=\Omega{(d^3\log{p})}$ for any tree-like graph in the paramagnetic phase, where $p$ is the number of variables and $d$ is the maximum node degree. When the same conditions are imposed directly on the sample covariance matrices, it is shown that a reduced sample size $n=\Omega{(d^2\log{p})}$ suffices. The obtained sufficient conditions for consistent model selection with Lasso are the same in the scaling of the sample complexity as that of $\ell_1$-regularized logistic regression. Given the popularity and efficiency of Lasso, our rigorous analysis provides a theoretical backing for its practical use in Ising model selection.
翻译:我们考虑了使用以邻里为基础的最小绝对缩水和选择操作员(Lasso)的高维Ising模型选择问题。 严格证明,在Ising模型的人口变量矩阵某些温和的一致条件下,可以用样本大小($ ⁇ Omega{(d ⁇ 3\log{p})}(d ⁇ 3\log{p})$)来为抛磁阶段中任何类似树形的图解进行一致的模型选择,其中,美元是变量的数量,美元是最大节点。当对样本变量变量矩阵直接施加同样的条件时,可以证明样本大小已经缩小了 $ ⁇ Omega{(d ⁇ 2\log{p}}}$。 与Lasso一致选择模型的足够条件与样本复杂性的大小相同,与Lasso相同。考虑到Lasso的受欢迎程度和效率,我们严格的分析为在选择Ising模型时的实际使用提供了理论支持。