A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization
DOI:
https://doi.org/10.1609/aaai.v31i1.10833Keywords:
feature selection, general framework, sparsity regularizationAbstract
A variety of feature selection methods based on sparsity regularization have been developed with different loss functions and sparse regularization functions. Capitalizing on the existing sparsity regularized feature selection methods, we propose a general sparsity feature selection (GSR-FS) algorithm that optimizes a ℓ2,r (0 < r ≤ 2) based loss function with a ℓ2,p-norm (0 < p ≤ 2) sparse regularization. The ℓ2,r-norm (0 <
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Published
2017-02-13
How to Cite
Peng, H., & Fan, Y. (2017). A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10833
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Section
Machine Learning Methods