scholarly journals A Fast Active Set Block Coordinate Descent Algorithm for $\ell_1$-Regularized Least Squares

2016 ◽  
Vol 26 (1) ◽  
pp. 781-809 ◽  
Author(s):  
Marianna De Santis ◽  
Stefano Lucidi ◽  
Francesco Rinaldi
Keyword(s):  
Least Squares ◽  
Coordinate Descent ◽  
Block Coordinate Descent ◽  
Active Set ◽  
Descent Algorithm ◽  
Regularized Least Squares ◽  
Coordinate Descent Algorithm

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.

scholarly journals Accelerated Asynchronous Greedy Coordinate Descent Algorithm for SVMs

2018 ◽  
Author(s):  
Bin Gu ◽  
Yingying Shan ◽  
Xiang Geng ◽  
Guansheng Zheng
Keyword(s):  
State Of The Art ◽  
Binary Classification ◽  
Coordinate Descent ◽  
Support Vector ◽  
Active Set ◽  
Descent Algorithm ◽  
Active Set Strategy ◽  
Coordinate Descent Algorithm ◽  
Vector Machines ◽  
Classification And Regression

Support vector machines play an important role in machine learning in the last two decades. Traditional SVM solvers (e.g. LIBSVM) are not scalable in the current big data era. Recently, a state of the art solver was proposed based on the asynchronous greedy coordinate descent (AsyGCD) algorithm. However, AsyGCD is still not scalable enough, and is limited to binary classification. To address these issues, in this paper we propose an asynchronous accelerated greedy coordinate descent algorithm (AsyAGCD) for  SVMs. Compared with AsyGCD, our AsyAGCD has the following two-fold advantages: 1) our AsyAGCD is an accelerated version of AsyGCD because active set strategy is used. Specifically, our AsyAGCD can converge much faster  than AsyGCD for the second half of iterations. 2) Our AsyAGCD can handle more SVM formulations (including binary classification and regression SVMs) than AsyGCD. We provide the comparison of computational complexity of AsyGCD and our AsyAGCD. Experiment results on a variety of datasets and learning applications confirm that our AsyAGCD is much faster than the existing SVM solvers (including AsyGCD).

Sign in / Sign up

Export Citation Format

Share Document