Online Streaming Feature Selection Algorithm Regularized by?2,1-norm
WU Zhonghua
ZHENG Wei
Abstract：High dimensional streaming feature data contain a mass of irrelevant and redundant information,which may greatly reduce the efficiency of learning algorithms. Feature selection algorithms play an important role in many application scenarios for speeding up machine learning algorithms,improving the generalization ability of learning models and avoiding the curse of dimen?sionality. In the scene where the feature space is unknown and dynamic,the traditional feature selection algorithm based on the stat?ic feature space is not suitable for low efficiency. In order to solve streaming feature selection problem that feature space is dynamic and unknown,the paper proposes the online streaming feature selection regularized by?2,1-norm. The paper constructes the feature selection model using the sparse property of the?2,1-norm and the insensitivity of the noise. Experimental results demonstrate that, compared with other streaming feature selection algorithms,the proposed feature selection algorithm has higher recognition perfor?mance and stability in multiple high-dimensional datasets.
Keywords：streaming featuresfeature selection?21-norm
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1306-1313 )