@Article{IJNAM-15-1-2,
author = {Zheng, Li and Song, Guohui and Yuesheng, Xu},
title = {A Fixed-Point Proximity Approach to Solving the Support Vector Regression with the Group Lasso Regularization},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2018},
volume = {15},
number = {1-2},
pages = {154--169},
abstract = {<p style="text-align: justify;">We introduce an optimization model of the support vector regression with the group
lasso regularization and develop a class of efficient two-step fixed-point proximity algorithms to
solve it numerically. To overcome the difficulty brought by the non-differentiability of the group
lasso regularization term and the loss function in the proposed model, we characterize its solutions
as fixed-points of a nonlinear map defined in terms of the proximity operators of the functions
appearing in the objective function of the model. We then propose a class of two-step fixed-point
algorithms to solve numerically the optimization problem based on the fixed-point equation.
We establish convergence results of the proposed algorithms. Numerical experiments with both
synthetic data and real-world benchmark data are presented to demonstrate the advantages of
the proposed model and algorithms.</p>},
issn = {2617-8710},
doi = {https://doi.org/2018-IJNAM-10561},
url = {https://global-sci.com/article/83106/a-fixed-point-proximity-approach-to-solving-the-support-vector-regression-with-the-group-lasso-regularization}
}