A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization

Authors

  • Hanyang Peng Institute of Automation, Chinese Academy of Sciences and University of Chinese Academy of Sciences
  • Yong Fan Perelman School of Medicine, University of Pennsylvania,

DOI:

https://doi.org/10.1609/aaai.v31i1.10833

Keywords:

feature selection, general framework, sparsity regularization

Abstract

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