基于图拉普拉斯的多标签类属特征选择

doi:10.12066/j.issn.1007-2861.2243

Abstract

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

CLC Number:

TP18

WU Zhejun, HUANG Rui. Multi-label label-specific feature selection based on graph Laplacian[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(2): 281-290.

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References 24

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

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