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

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

Machine Learning Methods

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 _2,r (0 <Êr ² 2) based loss function with a _2,p-norm (0 < p ² 2) sparse regularization. The _2,r-norm (0 <

10.1609/aaai.v31i1.10833