Indexado en

Licencia y uso

Citaciones

Cited 2 times in

Altmetrics

Grant support

Work with partial support from Spain's grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAM-CM. Also supported by project FACIL-Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2016, and the UAM-ADIC Chair for Data Science and Machine Learning. We gratefully acknowledge the use of the facilities of Centro de Computacion Cientifica (CCC) at UAM.

Análisis de autorías institucional

Compartir

Accelerated Block Coordinate Descent for Sparse Group Lasso

Publicado en:Proceedings of the International Joint Conference on Neural Networks. - 2018-10-10 (), DOI: 10.1109/IJCNN.2018.8489078

Autores: Catalina A; Alaíz C; Dorronsoro J

Afiliaciones

Resumen

© 2018 IEEE. In this work we transfer several recent acceleration techniques for the FISTA algorithm for composite convex opti- mization to the context of problems amenable to be solved by Block Coordinate Descent and, in particular, we show how these improvements can be used to speed up the training of the Sparse Group Lasso model. Experiments in several real life datasets illustrate how the proposed method can outperform the state of the art approach in terms of both the number of inner FISTA and outer block iterations required and, more importantly, the number of function and gradient evaluations needed to achieve a certain precision.

Palabras clave