Open AccessThe Sparse Group Log Ridge for the Selection of Variable Groups
Author(s) -
Song Yao,
Lipeng Cui,
Sining Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012012
Subject(s) - coordinate descent , group (periodic table) , feature selection , lasso (programming language) , group structure , group selection , selection (genetic algorithm) , elastic net regularization , variable (mathematics) , computer science , mathematics , artificial intelligence , algorithm , pattern recognition (psychology) , statistics , psychology , mathematical analysis , chemistry , organic chemistry , world wide web , psychotherapist
In recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.