Kuang Zhaobin, Geng Sinong, Page David
University of Wisconsin.
Adv Neural Inf Process Syst. 2017 Dec;30:720-731.
We discover a screening rule for -regularized Ising model estimation. The simple closed-form screening rule is a necessary and sufficient condition for exactly recovering the blockwise structure of a solution under any given regularization parameters. With enough sparsity, the screening rule can be combined with various optimization procedures to deliver solutions efficiently in practice. The screening rule is especially suitable for large-scale exploratory data analysis, where the number of variables in the dataset can be thousands while we are only interested in the relationship among a handful of variables within moderate-size clusters for interpretability. Experimental results on various datasets demonstrate the efficiency and insights gained from the introduction of the screening rule.
我们发现了一种用于正则化伊辛模型估计的筛选规则。这个简单的闭式筛选规则是在任何给定正则化参数下精确恢复解的块状结构的充要条件。在足够稀疏的情况下，该筛选规则可与各种优化程序相结合，以便在实际中高效地给出解决方案。该筛选规则特别适用于大规模探索性数据分析，在这种分析中，数据集中的变量数量可能达到数千个，而我们仅对中等规模聚类中少数变量之间的关系感兴趣，以便于解释。在各种数据集上的实验结果证明了引入该筛选规则所带来的效率和见解。
