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Safe screening for sparse regression with the Kullback-Leibler divergence

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 L1-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.
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Contributor : Cassio F. Dantas Connect in order to contact the contributor
Submitted on : Wednesday, April 21, 2021 - 11:47:35 AM
Last modification on : Monday, July 4, 2022 - 10:25:54 AM


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  • HAL Id : hal-03147345, version 2


Cassio F. Dantas, Emmanuel Soubies, Cédric Févotte. Safe screening for sparse regression with the Kullback-Leibler divergence. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2021, Toronto (virtual), Canada. ⟨hal-03147345v2⟩



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