Skip to yearly menu bar Skip to main content


Poster

A Screening Rule for l1-Regularized Ising Model Estimation

Zhaobin Kuang · Sinong Geng · David Page

Pacific Ballroom #176

Keywords: [ Graphical Models ]


Abstract:

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.

Live content is unavailable. Log in and register to view live content