| Paper ID | SPTM-21.4 |
| Paper Title |
SAFE SCREENING FOR SPARSE REGRESSION WITH THE KULLBACK-LEIBLER DIVERGENCE |
| Authors |
Cassio Dantas, Emmanuel Soubies, Cédric Févotte, IRIT, Université de Toulouse, CNRS, France |
| Session | SPTM-21: Optimization Methods for Signal Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [OPT] Optimization Methods for Signal Processing |
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| Virtual Presentation |
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| Abstract |
Safe screening rules are powerful tools to accelerate iterative solvers in sparse regression problems. They allow early identification of inactive coordinates (i.e., those not belonging to the support of the solution) which can thus be screened out in the course of iterations. In this paper, we extend the GAP Safe screening rule to the $\ell_1$-regularized Kullback-Leibler divergence which does not fulfil the regularity assumptions made in previous works. The proposed approach is experimentally validated on synthetic and real count data sets. |
