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A Safe Screening Rule for Sparse Logistic Regression
Jie Wang · Jiayu Zhou · Jun Liu · Peter Wonka · Jieping Ye

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper, we present a fast and effective sparse logistic regression screening rule (Slores) to identify the zero components in the solution vector, which may lead to a substantial reduction in the number of features to be entered to the optimization. An appealing feature of Slores is that the data set needs to be scanned only once to run the screening and its computational cost is negligible compared to that of solving the sparse logistic regression problem. Moreover, Slores is independent of solvers for sparse logistic regression, thus Slores can be integrated with any existing solver to improve the efficiency. We have evaluated Slores using high-dimensional data sets from different applications. Extensive experimental results demonstrate that Slores outperforms the existing state-of-the-art screening rules and the efficiency of solving sparse logistic regression is improved by one magnitude in general.

Author Information

Jie Wang (Arizona State University)
Jiayu Zhou (Arizona State University)
Jun Liu (SAS Institute)
Peter Wonka (KAUST)
Jieping Ye (Arizona State University)

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