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A novel one-layer recurrent neural network for the l1-regularized least square problem
- Source :
- Neurocomputing
- Publication Year :
- 2018
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Lyapunov function
Mathematical optimization
Total variation
Artificial neural network
Computer science
Cognitive Neuroscience
Lyapunov
Recurrent neural network
Total variation denoising
Convex
Regularization (mathematics)
Least squares
Computer Science Applications
03 medical and health sciences
symbols.namesake
030104 developmental biology
Quadratic equation
l-regularization
Artificial Intelligence
symbols
Differentiable function
Variable (mathematics)
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....ea6ef58b42e103badb8da32410da8ffb