کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10151158 | 1666107 | 2018 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel one-layer recurrent neural network for the l1-regularized least square problem
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A novel one-layer recurrent neural network for the l1-regularized least square problem A novel one-layer recurrent neural network for the l1-regularized least square problem](/preview/png/10151158.png)
چکیده انگلیسی
The l1-regularized least square problem has been considered in diverse fields. However, finding its solution is exacting as its objective function is not differentiable. In this paper, we propose a new one-layer neural network to find the optimal solution of the l1-regularized least squares problem. To solve the problem, we first convert it into a smooth quadratic minimization by splitting the desired variable into its positive and negative parts. Accordingly, a novel neural network is proposed to solve the resulting problem, which is guaranteed to converge to the solution of the problem. Furthermore, the rate of the convergence is dependent on a scaling parameter, not to the size of datasets. The proposed neural network is further adjusted to encompass the total variation regularization. Extensive experiments on the l1 and total variation regularized problems illustrate the reasonable performance of the proposed neural network.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 315, 13 November 2018, Pages 135-144
Journal: Neurocomputing - Volume 315, 13 November 2018, Pages 135-144
نویسندگان
Majid Mohammadi, Yao-Hua Tan, Wout Hofman, S. Hamid Mousavi,