A Feature Selection Method based on Sparse Regularization Deep Neural Network
Author:
Affiliation:
1. School of Computer Science and Technology, Anhui University,Hefei,China,230601
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10134761/10135179/10135182.pdf?arnumber=10135182
Reference18 articles.
1. Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization;nie;Proceedings of the 23rd International Conference on Neural Information Processing Systems,0
2. Extended adaptive Lasso for multi-class and multi-label feature selection
3. Discriminative Feature Selection via A Structured Sparse Subspace Learning Module
4. L2,1 Regularized correntropy for robust feature selection;he;2012 IEEE Conference on Computer Vision and Pattern Recognition,0
5. Learning a discriminant graph-based embedding with feature selection for image categorization
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