scholarly journals Block coordinate descent algorithm improves variable selection and estimation in error‐in‐variables regression

2021 ◽  
Author(s):  
Célia Escribe ◽  
Tianyuan Lu ◽  
Julyan Keller‐Baruch ◽  
Vincenzo Forgetta ◽  
Bowei Xiao ◽  
...  
Keyword(s):  
Variable Selection ◽  
Coordinate Descent ◽  
Block Coordinate Descent ◽  
Descent Algorithm ◽  
Error In Variables ◽  
Coordinate Descent Algorithm ◽  
Variable Selection And Estimation

scholarly journals The Sparse Group Log Ridge for the Selection of Variable Groups

2021 ◽  
Vol 2078 (1) ◽  
pp. 012012
Author(s):  
Song Yao ◽  
Lipeng Cui ◽  
Sining Ma
Keyword(s):  
Artificial Intelligence ◽  
Group Structure ◽  
Coordinate Descent ◽  
Block Coordinate Descent ◽  
Descent Algorithm ◽  
Mm Algorithm ◽  
Sparse Model ◽  
Coordinate Descent Algorithm ◽  
Variables Selection ◽  
Selection Of

Abstract 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.

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