收稿日期: 2018-12-10
网络出版日期: 2021-06-27
基金资助
上海市自然科学基金资助项目(16ZR1411100)
Label-specific feature-based multi-label manifold learning
Received date: 2018-12-10
Online published: 2021-06-27
多标签流形学习(multi-label manifold learning, ML$^{2}$)基于特征流形构建标签流形, 将标签逻辑值转换为实数值, 能更好地反映标签相关性, 提高分类性能. 但是, ML$^{2}$ 与多数多标签分类方法一样, 是基于数据的全部特征进行标签预测, 没有考虑不同特征对不同类别标签的鉴别能力. 因此, 提出一种基于类属特征的多标签流形学习分类(label specific feature based multi-label manifold learning, LSF-ML$^{2}$)方法. 首先, 利用标签数据优化类属特征重要度矩阵, 确定类属特征子集; 再将子集的特征流形映射到标签空间, 使标签从离散型变为数值型; 最后, 通过多输出回归实现分类. 实验结果表明, 所提方法性能优于多种多标签分类方法.
亢浏越, 黄睿, 孙广玲 . 基于类属特征的多标签流形学习分类方法[J]. 上海大学学报(自然科学版), 2021 , 27(3) : 525 -534 . DOI: 10.12066/j.issn.1007-2861.2132
Multi-label manifold learning (ML$^2$) constructs label manifolds based on feature manifolds and converts logical into numeric labels. This can better reflect the correlations between labels and improve classification performance. However, similar to most methods, ML$^2$ is based on all features and ignores different discriminabilities when different features are used to classify different labels. Therefore, a method we call label-specific feature-based multi-label manifold learning (LSF-ML$^2$) is proposed. First, the labels are used to optimise the feature importance matrix, which can determine the subset of label-specific features. Then, the feature manifold of the subset is mapped to the label space so that the logical labels can be converted into numeric labels. Finally, a multi-output regression is applied for classification. Experimental results show that the proposed method outperforms several existing multi-label classification methods.
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