基于<em>l</em><sub>2,0</sub>范数稀疏性和模糊相似性的图优化无监督组特征选择方法

doi:10.16451/j.cnki.issn1003-6059.202301003

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Abstract Most graph-based unsupervised feature selection methods choose l_2,1-norm sparse regularization of the projection matrix instead of non-convex l_2,0-norm constraint. However, the l_2,1-norm regularization method selects features one by one according to the scores, without considering the correlation of features. Therefore, an unsupervised group feature selection method for graph optimization based on l_2,0-norm sparsity and fuzzy similarity is proposed, and it simultaneously performs graph learning and feature selection. In graph learning, the similarity matrix with exact connected components is learned. In the process of feature selection, the number of non-zero rows of projection matrix is constrained to realize group feature selection. To solve the non-convex l_2,0-norm constraint, the feature selection vector with elements of 0 or 1 is introduced to transform the l_2,0-norm constraint problem into 0-1 integer programming problem, and the discrete 0-1 integer constraint is transformed into two continuous constraints to solve the problem. Finally, fuzzy similarity factor is introduced to extend the method and learn more accurate graph structure. Experiments on real datasets show the effectiveness of the proposed method.

Key words： Key Words Unsupervised Feature Selection 0-1 Integer Programming Graph Optimization Sparse Learning

Received: 23 October 2022

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TP391

Corresponding Authors: ZHOU Shuisheng, Ph.D., professor. His research interests include optimization theory and algorithm, pattern recognition and application, intelligent information processing and machine learning.

About author:: MENG Tiantian, master student. Her research interests include machine learning and optimization theory and algorithm.TIAN Xinrun, master student. Her research interests include optimization theory and algorithm, and pattern recognition and application.

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	MENG Tiantian
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	TIAN Xinrun

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MENG Tiantian,ZHOU Shuisheng,TIAN Xinrun. Unsupervised Group Feature Selection Method for Graph Optimization Based on l_2,0-norm Sparsity and Fuzzy Similarity[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(1): 34-48.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202301003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I1/34

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