Alternating Direction Multiplier Method for Matrix l2,1-Norm Optimization in Multitask Feature Learning Problems
Joint Authors
Hu, Yaping
Liu, Liying
Wang, Yujie
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-26
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
The joint feature selection problem can be resolved by solving a matrix l2,1-norm minimization problem.
For l2,1-norm regularization, one of the most fascinating features is that some similar sparsity structures can be employed by multiple predictors.
However, the nonsmooth nature of the problem brings great challenges to the problem.
In this paper, an alternating direction multiplier method combined with the spectral gradient method is proposed for solving the matrix l2,1-norm optimization problem involved with multitask feature learning.
Numerical experiments show the effectiveness of the proposed algorithm.
American Psychological Association (APA)
Hu, Yaping& Liu, Liying& Wang, Yujie. 2020. Alternating Direction Multiplier Method for Matrix l2,1-Norm Optimization in Multitask Feature Learning Problems. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1195483
Modern Language Association (MLA)
Hu, Yaping…[et al.]. Alternating Direction Multiplier Method for Matrix l2,1-Norm Optimization in Multitask Feature Learning Problems. Mathematical Problems in Engineering No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1195483
American Medical Association (AMA)
Hu, Yaping& Liu, Liying& Wang, Yujie. Alternating Direction Multiplier Method for Matrix l2,1-Norm Optimization in Multitask Feature Learning Problems. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1195483
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195483