基于弹性网的无向图模型的模型选择
Model Selection in Undirected Graphical Models with the Elastic Net
摘要：
随机场中的结构学习由于其在遥感、计算生物学、自然语言处理、蛋白质网络和社会网络分析等领域的难度和重要性而引起了相当大的关注。我们考虑的问题估计的概率图结构与高斯马尔可夫随机场（GMRF），伊辛模型和Potts模型，通过扩展以前的工作$l_1$正则化邻域估计，包括弹性网$l_1+l_2$罚款。此外，我们展示了数值证据，边缘密度在图形恢复过程中发挥了作用。最后，我们介绍了一种新的方法，通过利用成对邻域联合估计来增强邻域估计。
英文摘要：
Structure learning in random fields has attracted considerable attention due to its difficulty and importance in areas such as remote sensing, computational biology, natural language processing, protein networks, and social network analysis. We consider the problem of estimating the probabilistic graph structure associated with a Gaussian Markov Random Field (GMRF), the Ising model and the Potts model, by extending previous work on $l_1$ regularized neighborhood estimation to include the elastic net $l_1+l_2$ penalty. Additionally, we show numerical evidence that the edge density plays a role in the graph recovery process. Finally, we introduce a novel method for augmenting neighborhood estimation by leveraging pair-wise neighborhood union estimates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
