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Journal of Shanghai University >

2022 , Vol. 28 >Issue 2: 281 - 290

DOI: https://doi.org/10.12066/j.issn.1007-2861.2243

Research Articles

Multi-label label-specific feature selection based on graph Laplacian

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  • School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received date: 2020-03-07

  Online published: 2020-06-19

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Abstract

Multi-label feature selection, which can effectively removeredundant features and improve classification performance, has become an effective solution for the problem of "curse of dimensionality". However, existing multi-label feature selection methods select the same features for all labels without considering the intrinsic relation between labels and features. In fact, each label has label-specific features that reflect the specific attributes of the label. A feature selection method called multi-label label-specific feature selectionbased on graph Laplacian (LSGL) is proposed in this study. LSGL first obtains alow-dimensional embedding of instances for each class label based on Laplacianeigenmaps. Next, it obtains a projection matrix that can project samples from adata space to manifold embedding space through sparse regularization. It thendetermines the label-specific features of the corresponding class label bycoefficient analysis of the matrix. Finally, the label-specific featuresare used for classification. Experimental results of multi-label featureselection andclassification on five public multi-label datasets showed the effectiveness of the proposed algorithm.

Key words： multi-label learning; feature selection; label-specific feature; graph Laplacian

Cite this article

WU Zhejun, HUANG Rui . Multi-label label-specific feature selection based on graph Laplacian[J]. Journal of Shanghai University, 2022 , 28(2) : 281 -290 . DOI: 10.12066/j.issn.1007-2861.2243

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