scholarly journals An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers

2006 ◽  
Vol 31 (3) ◽  
pp. 176-189 ◽  
Author(s):  
S. Tosserams ◽  
L. F. P. Etman ◽  
P. Y. Papalambros ◽  
J. E. Rooda

Author(s):  
Ya-Fen Ye ◽  
Chao Ying ◽  
Yue-Xiang Jiang ◽  
Chun-Na Li ◽  
◽  
...  

In this study, we focus on the feature selection problem in regression, and propose a new version of L1support vector regression (L1-SVR), known as L1-norm least squares support vector regression (L1-LSSVR). The alternating direction method of multipliers (ADMM), a method from the augmented Lagrangian family, is used to solve L1-LSSVR. The sparse solution of L1-LSSVR can realize feature selection effectively. Furthermore, L1-LSSVR is decomposed into a sequence of simpler problems by the ADMM algorithm, resulting in faster training speed. The experimental results demonstrate that L1-LSSVR is not only as effective as L1-SVR, LSSVR, and SVR in both feature selection and regression, but also much faster than L1-SVR and SVR.







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