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A Screening Rule for l1-Regularized Ising Model Estimation
Zhaobin Kuang · Sinong Geng · David Page

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #176 #None

We discover a screening rule for l1-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.

Author Information

Zhaobin Kuang (University of Wisconsin, Madison)
Sinong Geng (University of Wisconsin Madison)
David Page (UW-Madison)

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